{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\".\\\\diyLogo.png\" alt=\"some_text\">\n",
    "<h1> 第七讲 绘图初步</h1>\n",
    "<a id=backup></a>\n",
    "<H2>目录</H2>  \n",
    "\n",
    "[7.1 Matplotlib简介](#Section1)      \n",
    "[7.2 Turtle简介](#Section2)      \n",
    "[7.3 PIL简介](#Section3)   \n",
    "[7.4 PyVista简介](#Section4) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = Section1> </a>\n",
    "## 7.1 Matplotlib\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.1.1 Matplotlib 画布\n",
    "\n",
    "figure(画布)、axes(坐标系)、axis(坐标轴)三者之间的关系。\n",
    "\n",
    "<img src=\"./img/matplotlib_fig.png\" alt=\"Matplotlib_Figure\" width=\"300\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./img/matplotlib_fig2.png\" alt=\"Matplotlib_Figure2\" width=\"300\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.__dict__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 创建figure(画布)的两种方式\n",
    "**隐式创建和显式创建**<br>\n",
    "+ **隐式创建figure对象**   \n",
    "  当第一次执行plt.xxx()这句绘图代码时，系统会去判断是否已经有了figure对象，如果没有，系统会自动创建一个figure对象，并且在这个figure之上，自动创建一个axes坐标系(注意：默认创建一个figure对象，一个axes坐标系)。   \n",
    "  也就是说，如果我们不设置figure对象，那么一个figure对象上，只能有一个axes坐标系，即我们只能绘制一个图形。\n",
    "  \n",
    "\n",
    "+ **隐式创建figure对象存在的问题**   \n",
    "  优点：如果只是绘制一个小图形，那么直接使用plt.xxx()的方式，会自动帮我们创建一个figure对象和一个axes坐标系，这个图形最终就是绘制在这个axes坐标系之上的。   \n",
    "  缺点：如果我们想要在一个figure对象上，绘制多个图形，那么我们就必须拿到每个axes对象，然后调用每个位置上的axes对象，就可以在每个对应位置的坐标系上，进行绘图，如下图所示。注意：如果figure对象是被默认创建的，那么我们根本拿不到axes对象。因此，需要我们显示创建figure对象。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.          5.02351155  8.68729618  9.9996678   8.60540338  4.88189209\n",
      " -0.16301361 -5.163796   -8.76688031 -9.99701037 -8.52122368 -4.73897526\n",
      "  0.3259839   5.30270815  8.84413462  9.99169621  8.43477945  4.59479904\n",
      " -0.48886756 -5.44021111]\n"
     ]
    }
   ],
   "source": [
    "from math import sin \n",
    "x=np.linspace(0,10,20)\n",
    "x=np.sin(x)*10\n",
    "y=np.random.randn(20)*10\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1)隐式创建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 隐式创建\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "y=1\n",
    "plt.plot(y,\"^--g\",label=\"Y\")\n",
    "plt.plot(x,'o-b',label=\"X\")\n",
    "plt.title(\"test matplotlib\")\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.xticks(np.arange(0,20,2))\n",
    "plt.yticks(np.arange(-25,25,5))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2)显式创建\n",
    "* figure 画布\n",
    "* axes 坐标系，一个画布上可以有多个坐标系\n",
    "* axis 坐标轴，一个坐标系中可以有多个坐标轴，一般都是二维平面坐标系，或者三维立体坐标系\n",
    "* title 标题\n",
    "* legend 图例\n",
    "* grid 背景网格\n",
    "* tick 刻度\n",
    "* axis label 坐标轴名称\n",
    "* tick label 刻度名称\n",
    "    * major tick label 主刻度标签\n",
    "    * minor tick label 副刻度标签\n",
    "* line 线\n",
    "* style 线条样式\n",
    "* marker 点标记\n",
    "* font 字体相关\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\AppData\\Local\\Temp\\ipykernel_12484\\2441909030.py:13: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  figure.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "figure = plt.figure()\n",
    "axes1 = figure.add_subplot(2,1,1)\n",
    "axes2 = figure.add_subplot(2,1,2)\n",
    "\n",
    "axes1.plot([1,3,5,7],[4,9,6,8],label=\"Line A\")\n",
    "axes2.plot([1,2,4,5],[8,4,6,2],label=\"Line B\")\n",
    "\n",
    "axes1.set(xlim=[0, 8], ylim=[0, 10], title='An Example Axes',\n",
    "       ylabel='Y-Axis', xlabel='X-Axis')\n",
    "axes2.set(xlim=[0, 6], ylim=[0, 10], title='An Example Axes',\n",
    "       ylabel='Y-Axis', xlabel='X-Axis')\n",
    "figure.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2.3 Multiple Axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Lower Right')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
    "axes[0,0].set(title='Upper Left')\n",
    "axes[0,1].set(title='Upper Right')\n",
    "axes[1,0].set(title='Lower Left')\n",
    "axes[1,1].set(title='Lower Right')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 基本绘图2D\n",
    "2.1 线\n",
    "plot()函数画出一系列的点，并且用线将它们连接起来。看下例子："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25f0e38e730>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "x = np.linspace(0, np.pi)\n",
    "y_sin = np.sin(x)\n",
    "y_cos = np.cos(x)\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(221)\n",
    "ax2 = fig.add_subplot(222)\n",
    "ax3 = fig.add_subplot(224)\n",
    "ax1.plot(x, y_sin)\n",
    "ax2.plot(x, y_sin, 'go--', linewidth=2, markersize=12)\n",
    "ax3.plot(x, y_cos, color='red', marker='+', linestyle='dashed')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_12484\\3063180553.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mhelp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "help(pd.Series)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "另外，我们可以通过关键字参数的方式绘图，如下例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "x = np.linspace(0, 10, 200)\n",
    "data_obj = {'x': x,\n",
    "            'y1': 2 * x + 1,\n",
    "            'y2': 3 * x + 1.2,\n",
    "            'mean': 0.5 * x * np.cos(2*x) + 2.5 * x + 1.1}\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "#填充两条线之间的颜色\n",
    "ax.fill_between('x', 'y1', 'y2', color='pink', data=data_obj)\n",
    "\n",
    "# Plot the \"centerline\" with `plot`\n",
    "ax.plot('x', 'mean', color='black', data=data_obj)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "发现上面的作图，在数据部分只传入了字符串，这些字符串对一个这 data_obj 中的关键字，当以这种方式作画时，将会在传入给 data 中寻找对应关键字的数据来绘图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.arange(100)\n",
    "y = np.random.randn(100)\n",
    "plt.scatter(x, y, color='pink', marker='*')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1.2 散点图\n",
    "只画点，但是不用线连接起来。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.arange(1000)\n",
    "y = np.random.randn(1000)\n",
    "plt.scatter(x, y, color='blue', marker='+')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1.3 条形图\n",
    "条形图分两种，一种是水平的，一种是垂直的，见下例子："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "x = np.arange(5)\n",
    "y = np.random.randn(5)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=plt.figaspect(1./2))\n",
    "\n",
    "vert_bars = axes[0].bar(x, y, color='lightblue', align='center')\n",
    "horiz_bars = axes[1].barh(x, y, color='lightblue', align='center')\n",
    "#在水平或者垂直方向上画线\n",
    "axes[0].axhline(0, color='gray', linewidth=1.5)\n",
    "axes[1].axvline(0, color='gray', linewidth=1.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "条形图还返回了一个Artists 数组，对应着每个条形，例如上图 Artists 数组的大小为5，我们可以通过这些 Artists 对条形图的样式进行更改，如下例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "vert_bars = ax.bar(x, y, color='lightblue', align='center')\n",
    "\n",
    "# We could have also done this with two separate calls to `ax.bar` and numpy boolean indexing.\n",
    "for bar, height in zip(vert_bars, y):\n",
    "    if height < 0:\n",
    "        bar.set(edgecolor='darkred', color='salmon', linewidth=3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1.4 直方图\n",
    "直方图用于统计数据出现的次数或者频率，有多种参数可以调整，见下例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(19680801)\n",
    "\n",
    "n_bins = 10\n",
    "x = np.random.randn(1000, 3)\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
    "ax0, ax1, ax2, ax3 = axes.flatten()\n",
    "\n",
    "colors = ['red', 'tan', 'lime']\n",
    "ax0.hist(x, n_bins, density=True, histtype='bar', color=colors, label=colors)\n",
    "ax0.legend(prop={'size': 10})\n",
    "ax0.set_title('bars with legend')\n",
    "\n",
    "ax1.hist(x, n_bins, density=True, histtype='barstacked')\n",
    "ax1.set_title('stacked bar')\n",
    "\n",
    "ax2.hist(x,  histtype='barstacked', rwidth=0.9)\n",
    "\n",
    "ax3.hist(x[:, 0], rwidth=0.9)\n",
    "ax3.set_title('different sample sizes')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "参数中density控制Y轴是概率还是数量，与返回的第一个的变量对应。histtype控制着直方图的样式，默认是 ‘bar’，对于多个条形时就相邻的方式呈现如子图1， ‘barstacked’ 就是叠在一起，如子图2、3。 rwidth 控制着宽度，这样可以空出一些间隙，比较图2、3. 图4是只有一条数据时。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1.5 饼图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3CA2LbjFRY1dYvLfxWxfnb7YxqqOGJ641kBRW/98R8Rrmb65v2j5WqhBoUDGpm45+sRqGx2s4UFg/Sn7xXjt6jYpbk3WXfE3nUq6YWWXr0iznvtJmL9tHnf38rTRCeDMJA8JnVZWXsv67zxlsSCHoErsHzsbpUvhkn51qOwyM05BWppBXpTC648kJPw1aFUPba9mY3fib0KYsJ6M7NJwkdExHLRuz6o9JCFFTY1fYmeukpFZha46THpEaSmoVZv9Yx1vXG5vsmk63VXMVLjTNdv4rKb24hrfXHvV0GUJ4lExHLHySoihsXLkUZ84u+oRcevfAqfbmOxn4fjV1DvDTw9JJJrqEa9iYVd+sH+nXsLk+0qIio7zx+93zqhQi/Rrm9Ug/NXlV9S0YwSYVi24xcfdXtdTaFe7uqWNMJy2/W1bLQ/31pJW5uOmTGuxOmDPMwO1dmq6V4Gsv6CI41dvrjjKhdyztwyzn31kILyRhQPik1L3b2PPLj/wmNAd1E91d2zlMza4ZfpTVKXxxwM7Ur+pYN+3km/npPfeKcuZzpzvfMROSdUw4pStgbbqDvQVO3hpnpNMbVSy5zUSUn4r+71UzpJ2GCMvlNwZWKUa+tfW47PO0JDaHizn/28/Ce1r+1MpCNAfpJhA+x1pXy6ZVXxGrKaKdktVk59VrVHQKUdM3RsO8kUZ6Rqr5v19sRJ34dP/rJ/pfFdSc+cn/VFF+KvKqGrYcFFS7zmhh+JXVofDAN3X8a7yJ1BIXDhcMba+lc5iGxFA1m8/RJXExtml64/DCzxFrDxWyLb3E02UI4RESBoTPObB9AznpRxjh17zLEiuA1QnxQSqi/FSsOuZwb7M5FdalOxjUpvF+94FxGlYda/gGvvKYg0FxZz/muZ+sXN9Jy1XRGpwucLhOhg+7E5xNdIfhN84BTXOiFui1VU03M6EQrYn3xXshzqGmqoLt676js18VEa7cJjvvkz/UcX0nLXGBaiqt9QMI16Y7WTHZgEqlYuYAPS+st5IQoiYhVM0L662YdSru6n6yif/upbXE+quYN7J+4N+fBugZ8kENL/5s5eYkLcsOOlh9zMnP95y5rO3+Aief7new6/f1fd5JYWrUKhXv77AR5afiYJGLfjGXP+CvRtHztbX3ZZ+npdp4tJhNR4sZ2DHU06UIcUVJGBA+Zc/mtRTkZHBT1DFowrvJ8qsUpiytJbeq/na/HpFqVkw2M+rEHQR/Hayn1qHwwLd1lNYqDGijYeUUM/6nzAGQWe5CrTrZWDcoTssnt5t4eo2VZ3600jFEzae3mxjQpuGvraIo3L+8jtfHGLDo689n0qlYeIuRP35bh9UBb40zEhtw+Q2BOzU9sNI8tyu2FK+vPszAjgM9XYYQV5SsTSB8RlVFGR/Pn024NYNbdWs9XU6r9BR/5OO6wQ2eK9/0GTWHN2EvyUal1WOITSZ46DR0oW3c+yiKQvmGxVTt/h5XXRX66ERCRv0BfXi7c75e9aENlK//D/ayXHRB0QQNmYI5cZB7e9X+HylbtwjFXodfj9EED/+de5ujPJ/8T58heup81IYzW1PO5eN7PbdugRCeIGMGhM84sO1nigtyGWRK9XQprVKdomWptc+Zz2ftw/+qG4j67StETnoOXE7yP3sGl+3k9MgVm7+gYutXhIycQdTdr6GxBFPw2TO4rDWNvp41J4WiZS9i6TqcmHvexNJ1OIXLXsR6/BAAzppySla8SfDw3xExcS5V+36g5uhW9/HF3y8geOi0iw4CAO+tb5rbTYVoLSQMCJ9QU1XBro0/0DYAohzZni6nVdqj7kaNcuZERpET5+LXfST68HboIzoQOm4mzopCbPn1oUtRFCq3LSNw4CTMnQehD29P2A2zcNmtVKesa/T1KrZ9jbF9bwIHTkQXGkfgwIkY2/WkYtsyABxleagMZizJQzBEJ2Js2wN7USYA1QfWotJoMXce1Oj5z2Xd4UKyShoPKkJ4GwkDwiek7NhEcV42vf2LPF1Kq7VCubC7CFzWagDURj+gvrneWV2KKf7kwEOVVocxrhvWnJRGz2PNOdjgGABT/FXuY7QhsSh2K7b8ozhrK7HlHkYf3h5nbSVl6z8mZNSMi7q+BtegwMebMy/5eCFaGxlAKLyew25n7+a1GM0W4m0bPF1Oq2RTNHxh63fe/RRFoXTNexjadEEf3h4AZ1UpAGpzUIN9NZYgHOUFjZ7LWV2KxnLmMc7q+vNpjH6E3fBnipa/huKwYel2HaYOfSj6dj7+fcbjKM+n4IvnwOUgcPBdWJKuufALBv67LYs/j0rAoPWOaZeFOBcJA8Lr5aQdovB4Jj0jwHziU6u4OPvVyZQr5+97L1n1DraCdKImv3TmRtVpkyUpypnPnXnQaYcoDZ4zJw5qMKCwLnMP9sIMQkbN4Pi79xN246NoLMHkfjgLY1y3M8LFuRRX2/h2by4Terc5/85CtHLSTSC83tEDO3E47CQqRzxdSqv1/QV0EZSseofa1M1E3vkC2oCTI/E1fsEAuE58ov+Vs6b8nG/OGkuwuxXgV65zHKM47JSsfJuQMX/EUZqL4nJibNsdXWgbdCGxWHMPnfcaTvfp1qaboVKIlkzCgPBqNVWVHNq9hZCgANrYZGW6S+FQ1PzX2vic/YqiULLqbWoObyTyjr+jC4pqsF0bGInGEkxt+s6Txzjt1GXtwxCb3Oh5DbFJDY4BqE3b2egxZRs/wdihD4aoTqC4wHVyIgnF5QBX44tCNWZreinFVdaLPk6I1kbCgPBqGYf3Ul5cQA+/ErQ4zn+AOEOKOoFixb/R7SWr3qZq/1rCbnwUtd6Ms6oUZ1UpLnv9m6hKpcK/782Ub/ovNYc3YitMp+ib+ah1BizJQ93nKVr+KqXrFrq/9+9zE3VpOyn/5XPsxVmU//I5dRm7COh78xk12AozqDn4E0HX/BYAbUgbUKmp3L2SmqNbsRdno49OuOhrd7oUvt+ff9HHCdHayJgB4bUUReHwnq2oVWo62g96upxWazXn7iKo2vktAPlLnmjwfOi4mfh1HwlAwIDbUBxWSla+jbOuCkNMZyImzm0wB4CjohBOmYHR2CaZsJv+Stn6/1C2/j9og6IIv+kxDDGdG7yOoiiUfP8Wwdfdh1pff+ujWmcgdNxMSla9jeK0EzJqBlr/S5tE6Lt9udw1oO0lHStEayEzEAqvVVqYx0evP0OwQeG3yn/Pu1ywOJNTUTHY/k/yXEGeLsVjdBoV254aRaDZu6dhFr5NugmE1zqWspuq8lLizVUSBC7RYXVHnw4CAHanwqoU6SoQ3k3CgPBKiqJwZO9WDCYzkY4cT5fTav1A4wMHfcmGVJmsSng3CQPCK1WVl1KYm4UlIJhwCQOXxKXAZ1ZZvQ9gS1qJp0sQollJGBBeqeB4BjWV5YRY9AQ6iz1dTqt0VNWOTFeop8toEXLKaskulbUKhPeSMCC8UuHxLFxOF1HkyXiBS7RWJV0Ep9qaLq0DwntJGBBeKetoCjqjkQi7dBFcqk9t0kVwKukqEN5MwoDwOjVVlRTkpGPxCyDcLssVX4o0Ykl1Rp1/Rx+yLb30/DsJ0UpJGBBep/B4BtWV5QT4mQl2Nr4qnmjcOrV0EZwuragau/PipzQWojWQMCC8TmFuFk67nTB1JWpkTq1L8V/roPPv5GMcLoWMYln1UngnCQPC62QfO4xGp8fsqvB0Ka1SFpHsd8Z6uowWKbWgytMlCNEsJAwIr+JyuSjMScdk8cfiqvR0Oa3Sz9JF0KijhdIyILyThAHhVWqrK7HW1aA3GLBIy8Al+a/cRdAoaRkQ3krCgPAqNZXl2GxWdHojZqeEgYuVq4Sxw9He02W0WGlF0jIgvJOEAeFVqivLsVut6AwG6Sa4BBs0/TxdQotWUm3zdAlCNAsJA8Kr1FSW43I60Wi00k1wCb60X+3pElo0CQPCW0kYEF6luqoClRo0ih2DUufpclqVIiWIjfYET5fRolVZHdgcMteA8D4SBoRXqSovBVTSKnAJNmr6erqEVqG0RloHhPeRMCC8SllRPlqdHp1L/mBfrC/tchfBhSiukn9bwvtIGBBepbykAJ3BiAppyr0YJYo/a+2dPV1Gq1BZZ/d0CUI0OQkDwmsoikJdbQ0ajRa1hIGL8oumD/Ln4MI4XTLFtfA+8tsvvIaiKKAoqFQq1IrT0+W0Kl875C6CC+WQMCC8kNbTBQjRVFxOJ8qJMLDXYODNwK6eLqlFs9sd1FRXo7MEcs2+3bx7YC0qTxfVQtlsdgx6PRFhIUSVRwPhni5JiCYlYUB4DZfLSXltNcVlJVS6XKR2kkmHzkkHmNVAJZ/130+5EsDknyowOhyerqzlys3AYpdbVoX3kTAgvIbT5WJHwXFyCo7jsAGdPF1R6/L9gAr2xMCf/6umvVXGXDRKrfF0BUI0ORkzILyGSq0mumMSfQYMZ0C/4Z4up1XKjYMn/6RmS68IFJV0GpyN2mjwdAlCNDkJA8JrqNVq1Co1ao0as8bs6XJaLYcGXrm+hH/dGkqd2ejpclocTXCwp0sQoslJGBBeQ6PWoFarcSkujIq8iV2uNYllPPI7NTltQz1dSosiYUB4IwkDwmtoNBrMJjMOhwMdOlSKNHNfrkJ/G3+eXM6qIZG41PLnAq0WTUCAp6sQosnJb7fwKgGWABwOBypU6NF7uhyv8e/BxfxjcgBVgX6eLsWjNEFBni5BiGYhYUB4lQD/AOyO+ulipaugae1qU8WD020cTIrwdCkeow0O8nQJQjQLCQPCqwT5B+Fw1t8nb1JMHq7G+9QYXMyeUMIn10fg0OnOu/+2mhoeyM5iaGoqXQ4dZHVlw7kfFEXhraJChqam0vvwIaZmZnDEaj3veVdWVjA+7Rg9Dx9ifNqxM877v4pyrjuaytVHDvNyQUGDbTl2G9cfO0qV8+JnqdRGRV/0MUK0BhIGhFexmC310xIDAYr07TaXL3uV8OQ9Roojgs65X43LRWeDkacjI8+6/f2SEhaVlvJ0ZCSftWtPmFbLvVlZVLsaf6PeVVvLI8ePc1NAIEvbteemgEBmHc9hd20tAKUOB7Pz8ng0PIJ/t4ljWUU566qq3Mf/LT+fWeER+Gkufr4AQ4f4iz5GiNZAwoDwKhaTBdWJSXUDXYEersa7pYfW8tC0Krb0iWx0ToIhfn78KTycUf7+Z2xTFIUPS0v4fUgoo/z9STAYmBcVTZ3iYnlFRaOv+2FpCQMtFu4PDaWDwcD9oaFcbbbwUWkJAFl2O35qNdcHBNDdZKK/2Uyqrb61YXlFOTqV6qz1XAh9h46XdJwQLZ2EAeFVQoNDQQVOp1NaBq4AhwZeGV3M27eHUGe+uG6ZbLudIqeTQRaL+zm9Wk1fs5ldJz7ln82u2loGmy0NnhtssbDzxDHt9HrqFIUDdXWUOZ3sq6ujs8FAmdPJm0VFPB1x9laKC2Ho2OGSjxWiJZMwILxKaFAoZqOZ2rpaAlwSBq6UtZ3KmTUdstqFXfAxRSfGdoRpGzbXh2k0FDka7yYocjgIPe2YUK2GohNjAAI1GuZFRfNEbi6TMtK5KSCAayx+vFxQwG+Dg8mx27k1PY2b0o7xfWXjLRBno+8gYUB4J1mbQHiVX8NATV0NEZYI9Ioem8rm6bJ8QpGfnUfuKmP6hkhGbihCc4ED9FSnrZWowHlXTzzfMSP9/Rl5SlfAlppqjtisPB0Zydhjx3glJoYwrYZJGRn0NZkJ1Z7/T6EmKAhtSMh59xOiNZKWAeFVjAYjYSFh1NTVABDikj/eV9r7g4v5x2/9qQw695wEYZr6N+DC01ZJLHY6z/jk3+A4rZai044pcTgJbWRAoM3lYm5+PnMio8i02XCi0M9sJl5voL1ez566xrskTmXs1u2C9hOiNZIwILxO2+i21J74Ax/uknXnPWF3TBUP/c5GSnLjcxK00ekI02jYVF3tfs6mKGyrqaGXqfHxB71MJjbWVDd4bkNNNb0bOebt4mKutVjoYjTiBBwn7jYBsCsKTuWsh53B3Lfvhe0oRCsk3QTC60RHRKO46v/CR7h8d4IcT6sxuHh6bCGD/cyM+qV+HoAcu52UujoCNRpidDruDg7h3ZJi2un1tNPrebe4GKNKzfhTpvx9PPc4EVots8Lr/19OCQ7m7sxM3isu5jo/P9ZUVfFLdTUftW13Rg1HrFa+q6zgy/b1twR20OtRq1R8UVZGmFZLms1Gd+OFTU5l7tvncn8kQrRYEgaE12kb3Ra9Tk+dtY5QQyhqRY1L5fJ0WT6pNq2W9/6Twnsnvn+xsH4CoFsCAnghOobpISHUKS7m5udR4XLRw2jkvbg4LOqTTf65dnuDJszeJjOvxMTwRlERbxQV0lav59WYWHqe1jKgKApz8vJ4PCIS84l1FYxqNS9ERfNcfh42ReHpiEgiL2DyJPR6jD16XM6PQogWTaUoygU2kgnROtTU1vDka08CEBEawWr9ago1hR6uSqidMHNNGAO256NqZX92TH360P7j/3i6DCGajYwZEF7HbDIT3yae8spyQLoKWgqXBl4bVcSCS5iTwNPM/c49XmDatGnccsstV6YYIZqBhAHhlRLjE7HZ628pjHHGeLgacap1ncr5872Q2f7C5yTwNL+hQz1dghDNSsKA8EpxUXHotDpsdhuhSihml9nTJYlTFFvs/OXOMlYMjcR5CWsEXEnqkBBMvXpd8vHr1q2jf//+GAwGoqOjefzxx3GccmtkZWUlkydPxmKxEB0dzeuvv86wYcOYOXOme58FCxaQkJCA0WgkMjKS22+//TKuSIgzSRgQXik+Lp7QoFBKykpQoaKNq42nSxJn8f8GFfPCb/2oDLq0tQKuhICRI1A1svbC+eTk5DBu3Dj69evH7t27efvtt3n//fd5/vnn3fvMmjWLDRs28PXXX7Nq1SrWr1/Pjh073Nu3bdvGww8/zNy5czl06BArVqxgyJAhl31dQpxK7iYQXslistAjqQerN64mKjyKOGcch7WHPV2WOIu9MdU8+Ds1j62IoMuBgvMfcIUFXH/9JR+7YMEC4uLieOutt1CpVCQlJXH8+HEee+wxZs+eTXV1NYsWLWLx4sWMGDECgA8++ICYmJNdW5mZmVgsFsaPH4+/vz/t2rWjd+/el31dQpxKWgaE1+qa0BUVKuwOO+GucIzKhd1PLq68WoOLOTeX8PH4cOx6vafLcVMFBWHu3/+Sj09JSWHgwIENWhYGDx5MVVUV2dnZHDt2DLvdTv9TXiMwMJDOnTu7vx81ahTt2rWjQ4cOTJkyhY8//piamppLrkmIs5EwILxW5/jOhASFuLsK2jrberokcR7LupfyxD16CqOCPV0KAIHjb0B1GWMaFEU5o4vh17u5VSpVg6/Ptg+Av78/O3bsYMmSJURHRzN79mx69uxJWVnZJdclxOkkDAiv5W/xp3tid0rLSwHo5Ojk4YrEhcgMqeOhuyvZ1C8K5RL76ptKyF13XdbxXbp0YePGjQ3e3Ddu3Ii/vz+xsbF07NgRnU7Hli1b3NsrKio4cuRIg/NotVpGjhzJSy+9xJ49e0hPT2fNmjWXVZsQp5IxA8Krde/cnXVb1mGz2wjUBRLhjKBA0/L6pUVDLg28PrKIazqEcN/yGkzVF7aYUFPS9uyJ4SKWLC4vL2fXrl0Nnrv//vuZP38+Dz30EA8++CCHDh3i2WefZdasWajVavz9/Zk6dSqPPvooISEhRERE8Oyzz6JWq92tBcuXL+fYsWMMGTKE4OBgvv32W1wuV4OuBCEul4QB4dW6JXQjJiKG/KJ84qLjSHAkSBhoRX7uUM6Be3U8uSyMtulFV/S1I+6ZdlH7r1279oyBfVOnTuXbb7/l0UcfpWfPnoSEhDB9+nSefvpp9z6vvfYaM2bMYPz48QQEBPDXv/6VrKwsjCfWTAgKCuLLL79kzpw51NXVkZCQwJIlS+jatetlX6MQv5LpiIXX+9+a/7Fk+RK6JnRFUSl8bfyaWtWV/6QpLs+0X0IZ81MRGqez2V9LCQoi+ef1qLRX/vNSdXU1sbGxvPrqq0yfPv2Kv77wTTJmQHi9Pt36EBQQRGl5KWrUdHR09HRJ4hIsvLqYv0/xoyK4+eckCLlj0hULAjt37mTJkiUcPXqUHTt2MHnyZABuvvnmK/L6QoCEAeEDosOj6dm5JwXF9d0DiY5EtIr0kLVG+6KreWi6lX1dm2+9CZfRSPi0ac12/rN55ZVX6NmzJyNHjqS6upr169cTFtZ6pmsWrZ90EwifsCtlF/+36P9oE9UGk9HEHu0e9uv2e7oscRlu3BvMHavK0VltTXpe85QptHvqySY9pxAtnbQMCJ/QtVNXEtsnkp2XDUCSIwmdcgHr2IsW63/dS3m8ieckcBoMtHnowSY7nxCthYQB4RN0Oh0jB4/E5XJRW1eLHj1JjiRPlyUuU1ZwHQ9NrWRD/6aZk8AyaSKagIAmqEyI1kXCgPAZvZN706VTF7JyswDo7OiMXmk5U9+KS+NSw/+NKOKNicHUWkyXfB6nyUTbh//UhJUJ0XpIGBA+Q6vVMnLwSABqamvQoaO7vbuHqxJNZUOHCmbeq5ARf2kD7wLunY7Gz9LEVQnROsgAQuFTHA4Hry18jf2H99O5Q2cUFL43fE+putTTpVG4vJD8z/MJHRVK9ORooH6O+oKvCihdV4qz2ompg4mYu2Mwxp570aXyreUULC3AVmBDH6En8rZIAvqcbP4u21hG3ud5KFaF4GuDibojyr3NVmgj/ZV0Os7piMZ06fPye9Ldm0O5/qciNI4Lm5PAGh1Nzx9Wo1LL5yPhm+RfvvApWq2WMdeMQavVUlZZhgoV/Wz9UCmenQO/5lgNJWtLMMY1fJMv+raI4u+Lif5tNB2f7YguUEf6y+k4axt/k6tJrSHr7SyCBgXRaW4nggYFkbkgk5qj9SvdOSod5HyQQ/SkaNo90o7SDaVU7qp0H3/8w+NE/iay1QYBgA8HFPPcBc5JoACRz86WICB8mvzrFz6nR+ceDLpqENm52bhcLkKVUDo6PTcRkbPOSfa/som9Jxa1+eSvpKIoFK8sJvzGcAL7BmJsYyT2vlhcVhflv5Q3er6ilUX4dfUjfHw4hhgD4ePD8Uv2o3hlMVD/yV9j0hA4IBBzBzOWZAt1x+sAKNtUhkqrIrBvYPNe9BVwIKp+ToK93SLPuZ918CCihw27MkUJ0UJJGBA+R6VScePwG4kKjyInPweAnvaeGBSDR+rJ/SgX/57++HX1a/C8vdCOo9yBX7eTz6t1aixJFmpSG1/Pvja1tsExAH7d/dzHGCINuGwuajNqcVQ5qE2rxRhnxFHloGBpAdG/jW7Cq/OsWp2L524s5sMbw7EbzhwsajMaSZo3zwOVCdGySBgQPikiNIJxQ8dRWV1JnbUOPXr62fpd8TrKfimjNqOWyNvP/PTqKHcAoA1oOFuiNkDr3nY2jnLHOY/RWDS0ua8N2f/O5tjcYwQNCsK/uz95n+YRMjIEe5Gd1NmpHHnqCOVbG2+BaE2Wdyvlsd/pKIhuOCeB6cEHMUU032yGQrQWEgaEz7q277V0T+xOek46iqIQ54oj3hF/xV7fVmwjd3Eube5vg1p/jl/F04czXMiQ3/McE9AngITnE0h8KZHICZFUpVRhzbYSMjSErLeziL4rmrYPtiXn/+XgqGg8eLQm2UFWHr67kvUDInEBFT26kzD9d54uS4gWQSZoFz7LoDdwy8hbSM9Jp6C4gMiwSPrY+1CoLqRKXdXsr1+XXoezwsnROUdPPumCmsM1FP9QTMI/EoD6T/q6oJOzJToqHWgDG//V1Qae2XJwrmNcdhe5H9WHEluBDcWpYEmqv8XOEGWg5mgNAb29YyIelxrevK6YvbGR/O33L6NqgomKhPAG0jIgfFrnDp0ZN3QcxaXF1NbVokPHYNtg1Erz/2pYuljo9HwnOs09+TDFmwi8OpBOczuhD9ejDdRStf9kMHE5XFQfrMbcydzoeU2dTA2OAajaV9XoMYVfF+LX3Q9TexOKSwHXyW2Ko+H33kDtUjF2zB8JadfO06UI0WJIGBA+b/Q1o+ndtTfHso7hcrkIUUK4yn5Vs7+uxqTB2MbY4KHSq9D6aeu/VqkIHR1K4f8KqdheQV12HTnv5aA2qAm8+uRo/+x3s8n7b577+7BRYVTtq6Lwm0Ksx60UflNI1YEqQkeHnlFDXU4d5VvKiby1fsyCIdoAKihZV0LlrkqsuVZMHS59Vr+WaHhtf8YNud3TZQjRokg3gfB5Br2BO8ffSW5BLhk5GcTHxZPgTKDUUcpR7dHzn6AZhY0Lw2VzcfzD4/WTDnU00f4v7RvMAWArtjUYI2BOMBP3hzjyv8in4MsC9BF64v4Qh7ljw5YBRVE4/sFxou6MQm2o/1yg1quJvTeW3I9yUewK0VOi0QV7z4JOHazt+duUV6V7QIjTyAyEQpywefdm3v3kXYICgggNDsWFi3X6deRp8s5/sGjxQmpDeH/c+3Rq28nTpQjR4kg3gRAn9O/Rn7FDxpJfnE9VdRVq1Ay2DSbA5R2D53yZyWri6d5PSxAQohESBoQ4QaVSccvIW7i277WkZadhtVnRo2eobajHJiQSl0/r0PKHtn9gZP+Rni5FiBZLwoAQp9DpdNx141307tqb1IxUnE4nfoofQ61D0Sne03fuK9RONb/x+w1TRk6RcQJCnIOEASFO42/xZ9qEaXSM68iRjCMoikKoEsow2zAJBK2I2qlmnGocM2+aiVYrY6WFOBcJA0KcRURoBFNvnUpYcBipGakoikKYK0wCQSuhdqoZWjeUv9zyF8ymxudkEELUkzAgRCM6tu3I9NunE+QfJIGgFVE71QypHcLjtz9OaPCZcysIIc4kYUCIc+ia0JV7J95LkH+Qu8sgzBXGMOsw9MqZq+AJz9I4NAypHcITv3mCmIgYT5cjRKsh8wwIcQH2H9nPe5+9R2llKQntElCpVFSoKlinX3dF1jEQ56e36hlmG8YjEx+RICDERZIwIMQF2n9kP+//932KSotIaJ+ARqPBipX1+vUUago9XZ5Ps1RZuM51HQ9OelCCgBCXQMKAEBfhcNphFn65kIycDBLiE9Dr9DhxskW3hXRtuqfL80lBJUGMNY9l+q3TiQqP8nQ5QrRKEgaEuEi5Bbks/HIhew/vJT4uHoupfrnfA9oD7NHuQVHJr9SVoFJUROVFMaHtBCbfNJkAP5kpUohLJWFAiEtQXlnOx19/zIYdG4iJiCE4MBiAIlURG/UbqVZXe7hC72a0G2mf3Z7b+93OhNET0OtkMKcQl0PCgBCXyGqz8uX3X/L9z99jMBhoG90WlUqFHTtbdFvI1GZ6ukSvFFIeQmJxIhNHT2TEwBGo1XJTlBCXS8KAEJfB5XKxccdGvvj+CwpLCunQtgNGgxGAY5pjbNdtx6FyeLhK76BxaYjKjqKvX1/uuOEOuiV283RJQngNCQNCNIGs3CyWfLOEXQd2ERUeRVhwGADVqmp26HaQrcn2cIWtW2h1KFHZUVzX8zpuH3s7IYEhni5JCK8iYUCIJlJbV8s3a7/h+5+/x+Fw0L5Ne3Ta+pkKc9Q5bNdtl7EEF8nsNBOZFUk7VTsmjJ7A0H5D0Wg0ni5LCK8jYUCIJqQoCrtSdrF01VJSM1IJDwknIjQClUqFAwf7tfs5qD2IS+XydKktmlpRE1MWQ8jxEHol9uK2MbfRIa6Dp8sSwmtJGBCiGVRWV7J642pWb1hNeWU57dq0c9+CWK2qZp92H2maNLkN8TQqRUVsbSyWdAvtg9tzw7AbGNxnsNwtIEQzkzAgRDNKy05j2epl7DywE51OR5uoNu43tkpVJfu0+8jQZPh8KFApKtrY2hCQEYAffgy+ajDjh48nPCTc06UJ4RMkDAjRzBwOB5t3b+a7n74jPScds9FMbFSsezxBhaqC/dr9ZGoyfa77QK2oibXHEpgViKHOQFLHJMYNG0f3xO6oVCpPlyeEz5AwIMQVUlNbw+bdm1m1YRUZxzPws/gRGxGLVqsFoI46jmqPkqpJpUZd4+Fqm5fFZaGdtR36bD0qq4qkjkmMHDSS3sm90elkeWghrjQJA0JcYdW11WzauYnVG1eTnZuN0WAkKjwKs8kMgIJCjjqHI9oj5KvzvaYLQaWoiHRF0qaqDa4cF4pLIaF9AqMGj+KqrlfJuAAhPEjCgBAeUlldyba92/h5+88cyzqG3WEnIjSC0KBQdxN5LbVkajLJ1GRSpC6C1tZyrkCYK4w2jjaYC81UlVRhMVlI6pjE4N6D6dWlFwa9wdNVCuHzJAwI4WEOh4ODxw6yefdmdhzYQWl5KX5mP8JCwrCYLA2CQY4mhxxNDoXqQuwqu4crPzudoiPcFU6MI4aAygAqiyqx2WyEh4Rzda+r6du9Lx3iOsiYACFaEAkDQrQgeYV57Dywky17tpCVl0VtXS0Wk4XQ4FD8zH7uN1AXLspUZRSqCynUFFKgLsCqsnqkZoNiIMIVQbgznDBnGKpyFaXlpdRZ6/C3+NMhrgP9uvejd9feBPkHeaRGIcS5SRgQohHTpk1j0aJFAGi1WkJCQujRowd33nkn06ZNa9YFcpxOJ2nZaaQcTWHngZ3k5OdQXVONRqshwBJAgH8AZqO5wafrKlUV5apyKtQVVKhOPNQV2FS2JqlJr+gJcAUQoJx4uALwd/pDNVRUVVBVXYWiKPj7+RPfJp5eyb1I7phMbGSstAII0cJJGBCiEdOmTSM/P58PPvgAp9NJfn4+K1asYN68eVx77bV8/fXX7jsBmpPL5SIrL4ujmUc5mnmUw2mHKasoo6auBrVKjZ/FD7PJjNloxmgwnvHGa8dOnaoOK1asqpMPJ04UGv76q1ChQYNBMZx8YMCoGNG4NNTW1VJTV0NNbQ3VtdWggJ/Fj5CgEJLik+gQ14GE9gnuWReFEK2DhAEhGjFt2jTKysr46quvGjy/Zs0aRowYwb///W/uvfdeMjMzeeihh/jhhx9Qq9WMHTuWN998k8jISPcxzz//PG+88Qa1tbVMmjSJsLAwVqxYwa5duwBYu3Ytf/3rX9m/fz86nY6uXbuyePFi2rVrd0ZdNruNnPwcsvOySctK40jGEcoqyqitq6XOVgeAWqXGYDCg0+rQarXotDr3o7EWDafTid1hx+6w43A46r+227HarCgoqFBhMpowGU2EBYeR2D6R9rHtaRPdhqiwKFkzQIhWTMKAEI1oLAwA9OrVi5iYGL755hv69OmDxWJh/vz5OBwOHnjgAfz9/Vm7di0AH3/8Mffeey8LFixg8ODBfPLJJ7z66qvEx8eza9cuHA4HYWFh3HfffcyYMQObzcaWLVsYPnw4bdu2PW+diqJQWV1JcVlx/aO0mILiAvKL8qmoqqC6ttr9Jm932HG5zj6xkUajcQcGvU6Pn9mPAL8AosKj3Hc5/Pr49TZIIYR3aP42TiG8UFJSEnv27GH16tXs2bOHtLQ04uLiAPjoo4/o2rUrW7dupV+/frz55ptMnz6de+65B4DZs2ezcuVKqqqqAKioqKC8vJzx48fTsWNHAJKTky+4FpVKRYBfAAF+AcS3iT9ju9PppNZa627idzgcZz2PXqfHbDRjMpowGozNOiZCCNGySBgQ4hIoioJKpSIlJYW4uDh3EADo0qULQUFBpKSk0K9fPw4dOsQDDzzQ4Pj+/fuzZs0aAEJCQpg2bRpjxoxh1KhRjBw5kokTJxIdHd0ktWo0GvzMfviZ/ZrkfEII7yPRX4hLkJKSQnx8vDsUnO7050/f5/TeuQ8++IBNmzYxaNAgPv30UxITE/nll1+ap3ghhDiNhAEhLtKaNWvYu3cvt912G126dCEzM5OsrCz39gMHDlBeXu5u6u/cuTNbtmxpcI5t27adcd7evXvzxBNPsHHjRrp168bixYub90KEEOIE6SYQ4hysVit5eXln3Fo4fvx47r77btRqNT169GDy5MkNBhAOHTqUvn37AvDQQw9x33330bdvX/cn/z179tChQwcA0tLSePfdd7npppuIiYnh0KFDHD58mLvvvtuTly6E8CESBoQ4hxUrVhAdHY1WqyU4OJiePXvyxhtvMHXqVPcAu6+++oqHHnqIIUOGNLi18FeTJ0/m2LFj/OUvf6Guro6JEycybdo0d2uB2Wzm4MGDLFq0iOLiYqKjo3nwwQf5/e9/75FrFkL4Hrm1UAgPGDVqFFFRUXz00UeeLkUIIaRlQIjmVlNTwzvvvMOYMWPQaDQsWbKE1atXs2rVKk+XJoQQgLQMCNHsamtrufHGG9mxYwdWq5XOnTvz9NNPc+utt3q6NCGEACQMCCGEED5Pbi0UQgghfJyEASGEEMLHSRgQQgghfJyEASGEEMLHSRgQQgghfJyEASGEEMLHSRgQQgghfJyEASGEEMLHSRgQQgghfJyEASGEEMLHSRgQQgghfJyEASGEEMLHSRgQQgghfJyEASGEEMLHSRgQQgghfJyEASGEEMLHSRgQQgghfJyEASGEEMLHSRgQQgghfJyEASGEEMLHSRgQQgghfJyEASGEEMLHSRgQQgghfJyEASGEEMLHSRgQQgghfJyEASGEEMLHSRgQQgghfJyEASHEGX766SduvPFGYmJiUKlUfPXVVw22K4rCnDlziImJwWQyMWzYMPbv33/e837xxRd06dIFg8FAly5dWLp06Rn7LFiwgPj4eIxGI3369GH9+vUNtr/yyitERkYSGRnJ66+/3mDb5s2b6dOnD06n8+IvWggfJmFACHGG6upqevbsyVtvvXXW7S+99BKvvfYab731Flu3biUqKopRo0ZRWVnZ6Dk3bdrEpEmTmDJlCrt372bKlClMnDiRzZs3u/f59NNPmTlzJk899RQ7d+7k2muv5frrryczMxOAvXv3Mnv2bJYsWcLixYt58skn2bdvHwB2u50ZM2bwzjvvoNFomvCnIYQPUIQQ4hwAZenSpe7vXS6XEhUVpfzjH/9wP1dXV6cEBgYq77zzTqPnmThxojJ27NgGz40ZM0a544473N/3799fmTFjRoN9kpKSlMcff1xRFEX59NNPlQEDBjTY/7PPPlMURVH+/ve/Kw8//PDFX6AQQpGWASHERUlLSyMvL4/Ro0e7nzMYDAwdOpSNGzc2etymTZsaHAMwZswY9zE2m43t27efsc/o0aPd+3Tv3p3Dhw+TmZlJRkYGhw8fplu3bqSmprJw4UKef/75prpMIXyKhAEhxEXJy8sDIDIyssHzkZGR7m2NHXeuY4qKinA6nefcJzk5mRdeeIFRo0YxevRo5s2bR3JyMjNmzOCll17i+++/p1u3bvTu3Zuffvrpsq9VCF+h9XQBQojWSaVSNfheUZQznruUY863z4wZM5gxY4b7+4ULF+Lv78/AgQPp3LkzW7duJTs7mzvuuIO0tDQMBsNFXZcQvkjCgBDiokRFRQH1n/Sjo6PdzxcUFJzxqf70405vOTj1mLCwMDQazTn3OV1RURFz587lp59+YvPmzSQmJpKQkEBCQgJ2u53Dhw/TvXv3S7pOIXyJdBMIIS5KfHw8UVFRrFq1yv2czWZj3bp1DBo0qNHjBg4c2OAYgJUrV7qP0ev19OnT54x9Vq1a1eh5Z86cyZ///GfatGmD0+nEbre7tzkcDrnFUIgLJC0DQogzVFVVkZqa6v4+LS2NXbt2ERISQtu2bZk5cyYvvPCC+1P4Cy+8gNls5q677nIfc/fddxMbG8u8efMA+NOf/sSQIUN48cUXufnmm1m2bBmrV6/m559/dh8za9YspkyZQt++fRk4cCDvvvsumZmZDboFfrVq1SqOHDnChx9+CED//v05ePAg3333HVlZWWg0Gjp37txcPyIhvIqEASHEGbZt28bw4cPd38+aNQuAqVOnsnDhQv76179SW1vLAw88QGlpKQMGDGDlypX4+/u7j8nMzEStPtn4OGjQID755BOefvppnnnmGTp27Minn37KgAED3PtMmjSJ4uJi5s6dS25uLt26dePbb7+lXbt2Deqrra3lwQcf5NNPP3W/RmxsLG+++Sb33HMPBoOBRYsWYTKZmuXnIy7P6a044tLpdLommVdDpSiK0gT1CCFakco6O6XVdoqrrZTV2KmxOXG4XFicNvod3wdqNSqtDrWfBW1wMJqQEDTBwahlMJ64DIqikJeXR1lZmadL8SpBQUFERUWddwDvuUjLgBBeyOVSyC6tJbWwktSCKo4WVJNaWEV2aQ2l1XZsTtdZj+unrSL28zmNnldlNqMNDUXfti36jh0wdOiAvkP9f7VhYc10NcJb/BoEIiIiMJvNl/XmJerDVU1NDQUFBQANBvReLAkDwmfMmzePL7/8koMHD2IymRg0aBAvvvhig35lRVH429/+xrvvvutu/v7nP/9J165dz3nuL774gmeeeYajR4/SsWNH/v73vzNhwoQG+yxYsICXX36Z3Nxcunbtyvz587n22mvd21955RVefvllAB5//HH+/Oc/u7dt3ryZBx54gC1btpy1SbDG5mBHRhlb0orZkl7C7qxyau0XP3iusrTsnNuVmhrsNTXYs7Ko3rChwTZNaCjmq67C3LcPpr59MSYloZJpgcUJTqfTHQRCQ0M9XY7X+LUrrKCggIiIiEvuMpAwIHzGunXr+OMf/0i/fv1wOBw89dRTjB49mgMHDmCxWICTc+4vXLiQxMREnn/+eUaNGsWhQ4ca9Ief6tc595977jkmTJjA0qVLmThxIj///LO7P/zXOfcXLFjA4MGD+de//sX111/PgQMHaNu2rXvO/eXLl6MoCuPHj2fUqFF069bNPef+u+++2+AXfU92GSv25bEhtYh9xytwui6/x09Rzt5icCGcxcVUrlpF5Ym7AdR+fph698YyeBD+I0eib9PmsusTrdevYwTMZrOHK/E+v/5M7Xb7JYcBGTMgfFZhYSERERGsW7eOIUOGoCgKMTExzJw5k8ceewwAq9VKZGQkL774Ir///e/Pep5JkyZRUVHBd999535u7NixBAcHs2TJEgAGDBjAVVddxdtvv+3eJzk5mVtuuYV58+bx2Wef8dprr/HLL7+49//LX/7Cb37zG1544QXy8/OZP38+2zNK+W5vLt/sOU5epa3JfybJNTm8tvL18+94CQydO+M/ZjQB11+PIT6+WV5DtFx1dXWkpaW5V6QUTacpfrYyz4DwWeXl5QCEhIQALXfO/ff+3weEDb2bfs+v4vZ3NvH+hvRmCQLNzXroEEVvvMmx68eRduttlH76Ga6aGk+XJYRAugmEj1IUhVmzZnHNNdfQrVs34Nxz7mdkZDR6rqaecx+obw1Qh3LnjeNw9ZzEGx9+TvmGxaDWEjLyfoxx3S7j6j2v7sAB8p59loJXXiHw5psJvutODB06eLosIXyWhAHhkx588EH27NnTYMKbX3lyzv3f3Xs//9mUxrs/pZI6ax41Th2hMUnk/HsG0Xe/hrOymKKvXyL29++j0uou6FpbMldlJaX/+Q+l//kP5quvJvTee/G7ZrCnyxJXWPvHv7lir5X+jxsu+php06axaNGiM54/cuQInTp1aoqyPE7CgPA5Dz30EF9//TU//fQTbU4Z1ObJOfftThf/b+0h3lmfRmmdgrOmnPINS4i860Wsxw+jC4lBFxKLLiQWxenAXpqDPrz9Zf0cWpqaX36h5pdfMPXtQ/hDD2MZ0N/TJQnhNnbsWD744IMGz4WHhzf43mazodfrr2RZTUbGDAifoSgKDz74IF9++SVr1qwh/rRBbJ6Yc//qqwfy/tqDDHjuO+atOkZpXf143tIf/o1/v1vQBoSB4kQ5dY59lxNclz7qv6Wr3badzKlTyZg6jZodOzxdjhBA/fihqKioBo8RI0bw4IMPMmvWLMLCwtzdfOvWraN///4YDAaio6N5/PHHcTgc7nNVVlYyefJkLBYL0dHRvP766wwbNoyZM2e691mwYAEJCQkYjUYiIyO5/fbbm/X6pGVA+Iw//vGPLF68mGXLluHv7+/+pB4YGIjJZEKlUl3ROffT0jP43p7EpyuONqizNm0n9tLjhI6vnwJYH52IoySb2qPbcFQWgVqDNiS2uX9cHlezeTMZd03Gf9RIIp98Et1lTKgiRHNZtGgRf/jDH9iwYQOKopCTk8O4ceOYNm0aH374IQcPHuS+++7DaDQyZ84coP7vwYYNG/j666+JjIxk9uzZ7Nixg169egH104E//PDDfPTRRwwaNIiSkhLWr1/frNchYUD4jF9v6xs2bFiD5z/44AOmTZsGcMXm3DdHtCVwwmzK9A2bGV12KyWr3yH8psdQqepfQ+sfRvDI31P03XxUGh2hN/wZtc53pgWuXLWaqg0bCf/jA4RMnYpKK3+2xJW3fPly/Pz83N9ff/31AHTq1ImXXnrJ/fxTTz1FXFwcb731FiqViqSkJI4fP85jjz3G7Nmzqa6uZtGiRSxevJgRI0YA9X+DYmJi3OfIzMzEYrEwfvx4/P39adeuHb17927W65PfKuEzLmRKDZVKxZw5c9wJ/mzWrl17xnO33377eZvx7r//9xSH9+ajHYXYlLNPDKLWGYi9719nPO/fcwz+Pcec8/zeTKmpoeDlVyj/ahlRc57F3KePp0sSPmb48OEN5gmxWCzceeed9O3bt8F+KSkpDBw4sMHg4MGDB1NVVUV2djalpaXY7Xb69z85JiYwMLDBTKijRo2iXbt2dOjQgbFjxzJ27FgmTJjQrBM2yZgBIa6AzQczGfr3b3h/e0mjQUCcn/XIETJ+O4W8uc/hqqvzdDnCh1gsFjp16uR+/DrI+NfZS391tjuJfv0golKpGnx9tn0A/P392bFjB0uWLCE6OprZs2fTs2fPZl3gScKAEM3IarUx+6MfmLxwFzm1EgKahKJQungxabffTkXqQU9XI0QDXbp0YePGjQ3e3Ddu3Ii/vz+xsbF07NgRnU7Hli1b3NsrKio4cuRIg/NotVpGjhzJSy+9xJ49e0hPT2fNmjXNVreEASGayeGM49z00v/4cH8dDiQINDV7VSV3bPg9Sw4u8XQpQrg98MADZGVl8dBDD3Hw4EGWLVvGs88+y6xZs1Cr1fj7+zN16lQeffRRfvzxR/bv38/vfvc71Gq1u7Vg+fLlvPHGG+zatYuMjAw+/PBDXC5Xg66EpiZhQIgm5nK5+GTVZm595xcOVcsc7M3BpVLx/gR/sijhhc0vMGvtLGrsMrWx8LzY2Fi+/fZbtmzZQs+ePZkxYwbTp0/n6aefdu/z2muvMXDgQMaPH8/IkSMZPHgwycnJ7nUFgoKC+PLLL7nuuutITk7mnXfeYcmSJeddPfVyyEJFQjShquoaXv70B/5zyIVT1frG5zbnQkVNaVmCho9vb9jnmhySzFsj3iLCHOGhqsS5yEJFjauuriY2NpZXX32V6dOnX/TxslCREC3I8fxCHnhzKR8eolUGgdYi1axiyYQzp4dOKUnhzm/u5GCJjCMQLdvOnTtZsmQJR48eZceOHUyePBmAm2++2WM1SRgQogkcTE3jd//8np/KglBU8mvVXKpV8MZkPa5GhmAU1BQw9buprMtad2ULE+IivfLKK/Ts2ZORI0dSXV3N+vXrCQsL81g98vFFiMugKAqbduzlL5/v5bgS7OlyvN4Hww3khTnPuU+No4Y//fgnnrn6GW5LvO0KVSbEhevduzfbt2/3dBkNSBgQ4hK5XC6+WfMzz63MoEAd4ulyvN5PMRp+GnDuIPArp+Lkb5v+hoLC7YnNO6e7EN5AwoAQl8DlcvH5dz/w0o/HKdJ6rmnPVxzXwb/vVAMXPt5ZQWHuprkAEgiEOA/p3BTiIkkQuLJswJsTTVj1F3/j06+B4PPDnzd9YUJ4EQkDQlwEl8vFl9/9wMs/5kgQuEI+66PnaFv7JR//ayD4KvWrpitKCC8jYUCIC+RyuVi6Yg3z1xyjUBt+/gPEZdsVrObr0a7LPo+Cwt82/Y3NuZuboCohvI+EASEugKIofPfjz7yzah/ZujaeLscnlGrgn1N0TXY+h8vBn9f+mWPlx5rsnEJ4CwkDQlyAX3bs4b0VW0g1dPR0KT7BCbxzg4lyy4XdPXChKm2V/HH1HymtK23S8wrR2sndBEKcx8HUNP619Af2qhNQJD9fEd8laNnZ9dLHCZxLdlU2f/rxT7w3+j30Gn2zvIa4SHMCr+BrlV/0IdOmTaOsrIyvvvqqwfNr165l+PDhlJaWEhQU1DT1eYj8ZRPiHHLyCnjvs/+xxRaHQ6YYviKOmlX8Z0LzvsbOgp28su2V5n0RIVoRCQNCNKKyqpoPPlvGT0VmatRmT5fjE2pU8MZdjU833JSWHFzC2qy1zf9Cwmd88cUXdO3aFYPBQPv27Xn11VcbbM/NzeWGG27AZDIRHx/P4sWLad++PfPnz3fvM2fOHNq2bYvBYCAmJoaHH374itQuH3WEOItfbyFcl1pKgbn5lg0VDS0caiA3vGnHCZzLMxue4YubvpCVDsVl2759OxMnTmTOnDlMmjSJjRs38sADDxAaGsq0adMAuPvuuykqKmLt2rXodDpmzZpFQUGB+xyff/45r7/+Op988gldu3YlLy+P3bt3X5H6JQwIcRabtu/m2427SDf38nQpPmN9tIa1A69cEAAos5bxxPon+Pfof6OWBabEOSxfvhw/P78GzzmdJ/+9vvbaa4wYMYJnnnkGgMTERA4cOMDLL7/MtGnTOHjwIKtXr2br1q307dsXgPfee4+EhAT3OTIzM4mKimLkyJHodDratm1L//79r8DVSTeBEGfIzs3nv9+s5pA+ETtXoL1akKuDd+/0zJ+jLXlb+H/7/p9HXlu0HsOHD2fXrl0NHu+99557e0pKCoMHD25wzODBgzly5AhOp5NDhw6h1Wq56qqr3Ns7depEcPDJBc5+85vfUFtbS4cOHbjvvvtYunQpDoej+S8OCQNCNFBntfLJshXsKdNSpvL3dDk+wQ689RsTVsPFTzfcVN7Z/Q5ZFVkee33R8lksFjp16tTgERsb696uKAoqlarBMYqinPXrxvaJi4vj0KFD/POf/8RkMvHAAw8wZMgQ7PbmubPmVBIGhDjFqp9+YdO+I2SbZD6BK+Wz3nqOtGv+P3bnYnVa+fuWv3u0BtG6denShZ9//rnBcxs3biQxMRGNRkNSUhIOh4OdO3e6t6emplJWVtbgGJPJxE033cQbb7zB2rVr2bRpE3v37m32+mXMgBAnZObk8t2PP5Pv3xmbIt0DV8LuIDXLxl7+dMNNYUPOBlamr2R0+9GeLkW0Qo888gj9+vXjueeeY9KkSWzatIm33nqLBQsWAJCUlMTIkSO5//77efvtt9HpdDzyyCOYTCZ3i8LChQtxOp0MGDAAs9nMRx99hMlkol27ds1ev4QBIagfCLR0xRoyqiDbHHz+A8RlK1PDP3+ro36+wZbhxa0vMjh2MBadxdOl+JZLmAiopbnqqqv47LPPmD17Ns899xzR0dHMnTvXfScBwIcffsj06dMZMmQIUVFRzJs3j/3792M0GgEICgriH//4B7NmzcLpdNK9e3f+97//ERoa2uz1q5TGOjKE8CEbt+1iwYefsdevL+Uug6fL8ZjkmhxeW/l6s7+OC3jpRiM7ul2ZwVEXY2qXqfyl3188XYbXqaurIy0tjfj4ePebn6/Lzs4mLi6O1atXM2LEiEs+T1P8bGXMgPB5ZRWVfL1qHcX6KJ8OAlfSik7aFhkEAD459An51fmeLkN4oTVr1vD111+TlpbGxo0bueOOO2jfvj1DhgzxdGkSBoRYvf4XjmXlkG1o6+lSfMIxk4qPbvV0FY2zOq38e++/PV2G8EJ2u50nn3ySrl27MmHCBMLDw90TEHmajBkQPi2vsJgfN22lLqgDlU7P/0J6u1oVvDHZgFPTMlsFfvXlkS+Z3m060X7Rni5FeJExY8YwZswYT5dxVtIyIHzauk1bKSgu55hK/uhfCQuHGDge3rKDAIDdZedfe/7l6TKEuGIkDAifdTy/kHWbd2AN6UCVUxrJmtuGKA0/Dmo5dw6cz7Kjy8ipyvF0GUJcERIGhM9au2krJaXlZKoiPV2K18vXwb/ual1/bhwuB0tSlni6DCGuiNb12ylEEzmeX8j6LTtRh8RR5pCxAs3JDrx5m5E6D043fKm+OvoVVqfV02UI0ewkDAiftHXXPkrLyjmujfJ0KV7v8556Dse3/HECZ1NuLee7tO88XYYQzU7CgPA5NbV1/Lx1F4aAEDLqZPKT5rQ3UM3ScS1juuFL9cnBTzxdghDNTsKA8Dm7DxzieH4BZea2KKjOf4C4JOVqeOu3rb8LZn/xfvYV7fN0GUI0KxlCLXyKoij8vHUnKo2ao1aZf765uIB/XW+kNKB1dg+cbumRpXQL6+bpMrxW90Xdr9hr7Z168SsATps2jUWLFgGg1WoJCQmhR48e3HnnnUybNg21uvV/rm79VyDERTiakcXB1DT0IXFyO2Ez+r6Dlm09vCMIAKzOXI3T1XpuixRNb+zYseTm5pKens53333H8OHD+dOf/sT48eNxOFr/v3UJA8Kn7Np/iOqaWgpUQZ4uxWulmVR8eLunq2haJXUlbM7b7OkyhAcZDAaioqKIjY3lqquu4sknn2TZsmV89913LFy4EIDMzExuvvlm/Pz8CAgIYOLEieTnN1zn4vnnnyciIgJ/f3/uvfdeHn/8cXr16uXevnbtWvr374/FYiEoKIjBgweTkZHR7NcnYUD4DJvNzrY9BwgM8Kd/QCVjQorpbK7GqJZPfE2lVgVv3GXAqfF0JU3vh4wfPF2CaGGuu+46evbsyZdffomiKNxyyy2UlJSwbt06Vq1axdGjR5k0aZJ7/48//pi///3vvPjii2zfvp22bdvy9ttvu7c7HA5uueUWhg4dyp49e9i0aRP3338/KlXzj22SdlLhM9KycsjNL8RgMOB0OogzQpzRymClnDybnrRaExl1RmpcXvhOdoV8eI2BnIjW32R6Nmuz1vL01U9fkT/MovVISkpiz549rF69mj179pCWlkZcXBwAH330EV27dmXr1q3069ePN998k+nTp3PPPfcAMHv2bFauXElVVRUAFRUVlJeXM378eDp27AhAcnLyFbkOaRkQPqNNdCTX9O+NQa8jNS2TA4ePkltQhMNhJ8ZgY3BQOXdG5jM+tIhulir8WvhiOi3NxigNP1zjva0sBbUFcleBOIOiKKhUKlJSUoiLi3MHAYAuXboQFBRESkoKAIcOHaJ///4Njj/1+5CQEKZNm8aYMWO48cYb+b//+z9yc3OvyHVIGBA+w2I2cd9dt/H3vz7IzHt/y6hrB6LTajiWkc3+w0c5nleAzW4jymDj6sAK7ogs4OawQnr4VRIgweCc8rXw7p3e/+dkS94WT5cgWpiUlBTi4+PdoeB0pz9/+j6K0nBmzg8++IBNmzYxaNAgPv30UxITE/nll1+ap/hTSDeB8G6r/wYRXSBxDBgDAAjw96Nfz67069mV6ppaDh1L58ChY+zYn0JaZg5Ol5NAf3/CQoIIN0C43k7/gEqK7VrSa02k1RllCuNT2IG3bjdSY/T+wLQ9fzvTu0/3dBmihVizZg179+7lz3/+M23atCEzM5OsrCx368CBAwcoLy93N/V37tyZLVu2MGXKFPc5tm3bdsZ5e/fuTe/evXniiScYOHAgixcv5uqrr27Wa5EwILyOy+Wi8HgmIRYdug3zQXGBxgAdhkGXm6DzODCHAPWtBVd1S+aqbsncdsMIDh/LJOXIUXbsO0hmTi52u4MAfz9Cg4MINRkJ1VXSJ6CSMruWtDoj6XVGiu16j16vp33ZU8+hVjrd8MXaVbALl+JCrfL+VhDRkNVqJS8vD6fTSX5+PitWrGDevHmMHz+eu+++G7VaTY8ePZg8eTLz58/H4XDwwAMPMHToUPr27QvAQw89xH333Uffvn3dn/z37NlDhw4dAEhLS+Pdd9/lpptuIiYmhkOHDnH48GHuvvvuZr8+CQPC6+zetIZ1Xy/mKv9ChignpsJ1WuHI9/UPtRbaX1sfDJJuBL9wAExGIz27JNKzSyITxo4gNSOLA4ePsn1vCsfzCqiz2fC3mAkLCSbQZKS3v4Pe/lVUODSk1xlJqzVRaNeBD81quDdQzRetfLrhi1Fpr+Rw6WGSQpI8XYpXuZSJgK60FStWEB0djVarJTg4mJ49e/LGG28wdepU96RDX331FQ899BBDhgxBrVYzduxY3nzzTfc5Jk+ezLFjx/jLX/5CXV0dEydOZNq0aWzZUt/9ZDabOXjwIIsWLaK4uJjo6GgefPBBfv/73zf79amU0zsshGjlvvj3yxzZu43bg/bSSX2e9ehVamg7EJJvqg8HATFn7GK3OziakUVK6jG270khJ7+AujorZrOJ8JBgLGaTux+w2qkmrdZEep2RfJu+1U13nFyTw2srX7+gfcvV8NgfDJQEeO+gwbN5vP/jTE6e7OkyWp26ujrS0tKIj4/HaJQ1QX41atQooqKi+Oijjy75HE3xs5WWAeFVKkqKOJ6RSnBoOO2UgvMfoLggY0P9Y8Xj0KbvyWAQ3B4AnU5LUqd4kjrFc+PIoaRlHedgahpb9+wnJzefjOw6TCYjYSHB+FvMdPOrpptfNTVONRknWgxyW2EwOJdfpxsu8ZLphi/G7oLdEgbEJampqeGdd95hzJgxaDQalixZwurVq1m1apWnS5MwILxLTvphqstLiW8fhq7SfpFHK5C9tf6x6hmI6lEfCrrcAmEJQP285AnxbUmIb8u4664hIzuXg0fT2bZnP5k5eWTm5GIyGAgNCSLQ349kSw3JlhrqXCoyTwSDHKsBVysPBivjvWu64YtxtPyop0sQrZRKpeLbb7/l+eefx2q10rlzZ7744gtGjhzp6dIkDAjvkn3sMKhUhLmKLv9keXvqH2ueh/Dk+mCQfBNE1S9Yo9Fo6NCuDR3atWHssEFk5+aTkprG9j0HSM8+TvbxfAwGPWEngkGiuZZEcy22X4NBnZFsqwGn0roGo6UbVSz6jaer8JzMikwZRCguiclkYvXq1Z4u46wkDAivoSgKx9MPY7T4E+I40rQnL0yBdSmw7kUI6QjJN0KXmyH2KgDUajVtY6NpGxvN6CEDyckr4GBqGjv2HeRYRhbH8wrQarWEhQQTHOhPJ3Mtncy12F0qsq0G0uqMZNUZsbfwYFCrgjfuNOD04XkX6px1HK86Thv/Np4uRYgmI2FAeI2q8lIqSoswmf0Iceaf/4BLVXIUNsyvfwS2PREMboK4AaBSoVKpaBMdSZvoSEZcM4C8wmIOHU1n576DHD6WzsHCIjQaDaHBgQQHBhJvqiPeVIdDgRyrgfQT0yLbWmAw+Giwgewo3w0CvzpWfkzCgPAqEgaE1ygtzKW2poqgkDCCKguvzIuWZ8Iv/6x/+EVB8vj6FoN2g0GtQaVSER0RRnREGEOv7kNhSSmHUtPZdeAQKalpHDqahlqlIiQ4iJDgQNoZrbQzWnEpcPxEi0FGnZG6FrBewqYINauv9a07BxqTVp7GkDZDPF2GEE1GwoDwGsUFuTgdDkLUlWjxwKfXqjzY+l79wxwGSeMg+WboMBQ0OlQqFRGhIUSEhnDtgKsoLi3n0NE0dqccZv+hoxw5Vr9MaUhQICHBgbQxQptTFlJKP3HLoicWUirQwr8ma6m/j0DkVJ3nllUhWhkJA8JrFOVmo1KpCXE0YxfBhaopgh0f1j+MgZB4fX2LQcfrQFd/H3BocCCD+vZiUN9elFVUcuhoOnsPHmFPymFS0zJRFIXgoEBCgwOJMUCMwcZApZx8m570E7MfVjmb/1fYAbx1q29MN3yhyurKPF2CEE1KwoDwCoqicDzjCEazhRBnqqfLaaiuHPZ8Uv/Q+0HC6PoxBgmjQW8BICjAnwG9uzOgd3cqq6o5eDSd/YeOsuvAQY5lZON0uQgO8Cc0JIgoA+7FlAptOvfshxXNFAy+7K7nYEcJAqcqtZZ6ugQhmpSEAeEVqivK6gcPWvzwd5Z7upzG2apg/5f1D60JOo2obzFIHOteSMnfz3LKQkqjOJyWwf5DR9mxL4X0rOM4nI4zFlLqd8pCSul1RkqbaCGl/QFqPh/nxJemWL4QZdYyT5cgRJOSMCC8QklhLrXVVQSGhGOorvF0ORfGUQsHl9c/NPr6hZSSb4KkGxospNS7axK9uyZx27gRHEnL5MDhXxdSysNut+Pv50dYyGkLKTk07hUWL3UhpQo1vPVbPailVeB0pXXSMtCUUpKSr9hrJR9Muehjpk2bRllZGV999VXTF9RCSBgQXqGqrASX04FWp8eg1Hq6nIvntMGRlfWP5TOh/TX1wSD5RvCLAOoXUuqRnEiP5JMLKaUcOca2PQc4nleA1WbD75SFlHr5V9HLv4pKh6Z+hcVaEwUXuJCSC3h3jJHiQAkCZyMtA8LbtLwbmYW4BNa6WqB+zS2DqxWGgVO5HHBsLXwzC17tDP/vevjlbSg/OYLdYNDTNbEjt98wiuf+8kce/+PvmHTjGCLDQskrLGL/oaOkZeVQWVWNn8ZBD79qbgov4s7IfAYGlBOlt6LiLGuUnVi3bHW8li29JAg0xuq0eroE0UKsW7eO/v37YzAYiI6O5vHHH8fhOPm7U1lZyeTJk7FYLERHR/P6668zbNgwZs6c6d5nwYIFJCQkYDQaiYyM5Pbbb7/i1yEtA8IrWOtqQFGhVhzouNg1CVowxQWZG+sfK56A2D4np0UOiQcaLqQ0fuQQ0rOPk5Kaxtbd+8nJLSAjOxez2UhYcBD+fha6+lXT9ZSFlNLrjOTU6kCtwWa1kWFUsfDK/y0SotXJyclh3LhxTJs2jQ8//JCDBw9y3333YTQamTNnDgCzZs1iw4YNfP3110RGRjJ79mx27NhBr169ANi2bRsPP/wwH330EYMGDaKkpIT169df8WuRMCC8Qm1NNYrKC1oFzkmBnG31j1WzIap7/TwGXW6G8ESgfiGlTu3b0ql9W8YNv4bMnLz69RL2HiAzJ5fM43lnXUhpwxdf4gxtj0Ov5407DTi00iogxPksWLCAuLg43nrrLVQqFUlJSRw/fpzHHnuM2bNnU11dzaJFi1i8eDEjRowA4IMPPiAm5uRS6ZmZmVgsFsaPH4+/vz/t2rWjd+/eV/xaJAwIr1BTUY5Go22d4wUuVd7e+sePz0N40smll6O6A/ULKcW3jSW+bax7IaWDR9PZvucAaVk55OTmo9frCAsJBlsV2iM/YuwVSZpMNyzEBUlJSWHgwIGoVCfH4QwePJiqqiqys7MpLS3FbrfTv39/9/bAwEA6d+7s/n7UqFG0a9eODh06MHbsWMaOHcuECRMwm81X9FokDAivUF1Vhlar8/KWgXMoPFj/+OklCOlwykJKfYCGCymNuvZqcvIKOHQ0nR37UjiankWNtQKtRUdy3z6k4fm11YVoDRRFaRAEfn0O6pcrPvXrs+0D4O/vz44dO1i7di0rV65k9uzZzJkzh61btxIUFNS8F3AKCQPCK9RUVqDR6TAoreS2wuZUcgw2/F/9IzCuPhgkn1hISa1usJDSdYP7k19Uwt+0+djqqtAbDZ6uXohWo0uXLnzxxRcNQsHGjRvx9/cnNjaWoKAgdDodW7ZsIS4uDoCKigqOHDnC0KFD3efRarWMHDmSkSNH8uyzzxIUFMSaNWu49dZbr9i1SBgQrZ7T6aSupgqtVne28fG+rTwLfllQ//CLhKQTCym1v8a9kFJUeCixMVFk52V7utpWw1/n7+kSxBVWXl7Orl27Gjx3//33M3/+fB566CEefPBBDh06xLPPPsusWbNQq9X4+/szdepUHn30UUJCQoiIiODZZ59FfSKUAyxfvpxjx44xZMgQgoOD+fbbb3G5XA26Eq4ECQOi1bPV1eJ0ONDodDhVTTPznleqyodt79c/onvC73/ydEWtVpAxyNMleJVLmQjoSlu7du0ZA/umTp3Kt99+y6OPPkrPnj0JCQlh+vTpPP300+59XnvtNWbMmMH48eMJCAjgr3/9K1lZWRiN9WuUBAUF8eWXXzJnzhzq6upISEhgyZIldO3a9Ypen4QB0eqd2h/nkDBwYaxVnq6gVQs2BHu6BHEFLVy4kIULFza6fcuWLY1u8/f35+OPP3Z/X11dzd/+9jfuv/9+AK655hrWrl3bVKVeMgkDotXTaHWo1GpcLhdO+SctrgBpGRAXaufOnRw8eJD+/ftTXl7O3LlzAbj55ps9XFlD8pdTtHoarRa1WoPTaZeWAXFFBBmCPF2CaEVeeeUVDh06hF6vp0+fPqxfv56wsDBPl9WAhAHR6qnVarR6PfZqKw6V/JMWzS/UFOrpEkQr0bt3b7Zv3+7pMs5L1iYQXkGnN9R3E0jLgLgC2ge093QJQjQpCQPCK+h0BlxOJw5p7BJXQHxgvKdLaLVOnXBHNI2m+JlKGBBeQW8woJxoGXBdwBK9QlyODoEdPF1Cq6PT1bfa1dTIxGBN7def6a8/40shH6OEV9DqDbhcThSVmhq1P36uCk+XJLxUiDGEQEOgp8todTQaDUFBQRQUFABgNpvPmKZXXBxFUaipqaGgoICgoCA0Gs0ln0vCgPAKBqMZl9MJQJUmSMKAaDYyXuDSRUVFAbgDgWgaQUFB7p/tpZIwILxCYEgYLld9GKhUBxFFpocrEt4qMTjR0yW0WiqViujoaCIiIrDb7Z4uxyvodLrLahH4lYQB4RX8AkPgxCCaKk2QZ4sRXq1PZB9Pl9DqaTSaJnkDE01HBhAKr+AfFAwqNS6nk3KN3AMums9VkVd5ugQhmpyEAeEV/AKCMRhN2Ky1lGla1sxewnu08WtDhDnC02UI0eQkDAivEBASjsFkpq62hhpNAFaV0dMlCS8kXQTCW0kYEF7BYDQRHBZJXU01gLQOiGYhYUB4KwkDwmtExnXAVls/+UaRNtrD1YiWrHB5Ifum7SP341z3c4qikL80n4MzD7L/vv0cm3eMupw693YVKgbFDDrjXF988QVdunTBYDDQpUsXli5desY+CxYsID4+HqPR6F6o5lSvvPIKkZGRREZG8vrrrzfYtnnzZvr06YPzxK2zQjQHCQPCa4RERKNQf0dBrl6mixVnV3OshpK1JRjjGnYlFX1bRPH3xUT/NpqOz3ZEF6gj/eV0nLX1b8LJIclEWiIbHLNp0yYmTZrElClT2L17N1OmTGHixIls3rzZvc+nn37KzJkzeeqpp9i5cyfXXnst119/PZmZ9be/7t27l9mzZ7NkyRIWL17Mk08+yb59+wCw2+3MmDGDd955R0bfi2YlYUB4jZCIaLRaHTZrHYXaGOwqvadLEi2Ms85J9r+yib0nFrX55J8/RVEoXllM+I3hBPYNxNjGSOx9sbisLsp/KQdgZLuRZ5xv/vz5jBo1iieeeIKkpCSeeOIJRowYwfz58937vPbaa0yfPp17772X5ORk5s+fT1xcHG+//TYAKSkp9OjRg+uuu44RI0bQo0cPUlJSAHj55ZcZMmQI/fr1a8afihASBoQXiWwTj39QCFXlpSgqDXnaOE+XJFqY3I9y8e/pj19XvwbP2wvtOMod+HU7+bxap8aSZKEmtb7raVS7UWecb9OmTYwePbrBc2PGjGHjxo0A2Gw2tm/ffsY+o0ePdu/TvXt3Dh8+TGZmJhkZGRw+fJhu3bqRmprKwoULef755y//woU4DwkDwmsYjCbaJXajurwUkK4C0VDZL2XUZtQSeXvkGdsc5Q4AtAEN52HTBmhxlDvoFNCJ9oHtzzguLy+PyMiG54uMjCQvLw+AoqIinE7nOfdJTk7mhRdeYNSoUYwePZp58+aRnJzMjBkzeOmll/j+++/p1q0bvXv35qeffrrk6xfiXGQGQuFV4joms2vjD7hcLnJ17T1djmghbMU2chfn0v4v7VHrz/EZ6PR1c06sDDu+0/jGDzltsR1FUc547nz7zJgxgxkzZri/X7hwIf7+/gwcOJDOnTuzdetWsrOzueOOO0hLS8NgMDR+DUJcAgkDwqtEt+uIyeJPbVUF6oAgyjUhBDpLPF2W8LC69DqcFU6Ozjl68kkX1ByuofiHYhL+kQDUtxDogk4uA+uodKAL1DEhYcJZzxsVFeX+hP+rgoICd0tAWFgYGo3mnPucrqioiLlz5/LTTz+xefNmEhMTSUhIICEhAbvdzuHDh+nevftF/wyEOBfpJhBeJSQihtDIWCp/7SqQ1gEBWLpY6PR8JzrNPfkwxZsIvDqQTnM7oQ/Xow3UUrW/yn2My+Gi+mA1vfv2JsQYctbzDhw4kFWrVjV4buXKlQwaVH8Lol6vp0+fPmfss2rVKvc+p5s5cyZ//vOfadOmDU6ns8GCPg6HQ24xFM1CWgaEV1GpVHTs0ovsowcByNF3JKluh4erEp6mMWnQtGl4a55Kr0Lrp8XYpv4Ww9DRoRT+rxBDpAF9pJ7C5YWoDWrmPjzXfczdd99NbGws8+bNA+BPf/oTQ4YM4cUXX+Tmm29m2bJlrF69mp9//tl9zKxZs5gyZQp9+/Zl4MCBvPvuu2RmZjboFvjVqlWrOHLkCB9++CEA/fv35+DBg3z33XdkZWWh0Wjo3Llzk/98hJAwILxOdLtOqLUa7DYb+bo4KtVB+LvKPF2WaOHCxoXhsrk4/uFxnNVOTB1NDHp6EIM7DHbvk5mZiVp9skF10KBBfPLJJzz99NM888wzdOzYkU8//ZQBAwa495k0aRLFxcXMnTuX3NxcunXrxrfffku7du0avH5tbS0PPvggn376qfs1YmNjefPNN7nnnnswGAwsWrQIk8nUzD8J4YtUinJi3VchvERdTTUfvPw4ToeDkIhokmq3cVXNOk+X1bKEdISHT7aYPL/gebLzsgmODWa5cbkHC2tZnu7/NJOSJ3m6DCGanYwZEF7HaLaQ1HMAFaVFKIrCMUNXHNIIJi5SoDaQWxJv8XQZQlwREgaEV+rcawBGk4Xa6kpsahMZBulnFRfnd11/h0Ejt/AJ3yBhQHilqLYdieuUTElB/UI0R4y9PFuQaFUC1YFM6T7F02UIccVIGBBeSa1W07XvNbgcDhx2OyXaKIo1Z7+vW4jTTe86HZ1Gd/4dhfASEgaE1+rYpTehUbGUFtVP+CKtA+JCBGuCmdJLWgWEb5EwILyWwWSma79rqS4vRVEUMgydqVPJbVni3B7q9RBatQw4Fb5FwoDwaok9+mEJCKKqvASnSsde89lnfRMCoJOhE7/p9htPlyHEFSdhQHi10MhYOnbpRUlBLoqikGroQbnm7FPLCt+mVtT87dq/eboMITxCwoDwaiqVil6DR2E0+1FVXoKiUrPTPNTTZYkWaGz4WHrE9vB0GUJ4hIQB4fVi4xPp0mcwxfnHURSF4/oO5Oraeros0YIEKAHMHjHb02UI4TESBoTXU6lU9Bs2jsCQcEpPzDuw0zwM1xmL1wufpMBjvR/DYrR4uhIhPEbCgPAJIRHR9L5mFBWlRTidDsq04aQZunq6LNECDPUbyk09b/J0GUJ4lIQB4TN6DRpBZFw8hcczAdhtHowdmVjGl4U5wnhu9HOeLkMIj5MwIHyG2S+AfsNuwFZXi81aR53aT2419GFap5a5V88lOCDY06UI4XESBoRPSb5qEG0TulKQnQ7AQWMf8rVxni1KXHkK3Bl1J9d2vdbTlQjRIkgYED5Fp9czYMSNaHU6KkqLQaVik99YbCpZnc6X9FH1YeaomZ4uQ4gWQ8KA8DkdknvRa/BISvJzcNht1GgC2Ga5ztNliSskpjaGF294Eb1O7+lShGgxJAyIFuHtt9+mR48eBAQEEBAQwMCBA/nuu+/c2xVFYc6cOcTExGAymRg2bBj79+8/73m/+OILunTpgsFgoEuXLixduhSVSsXAURNom9CV4xmprPxpM7fM+x7D3yvp824V6zMcDc7xykYrka9UEvlKJa9vsjbYtjnbQZ93q3C6lKb5QYhmFVAbwPPXPE9kmKxgKcSpJAyIFqFNmzb84x//YNu2bWzbto3rrruOm2++2f2G/9JLL/Haa6/x1ltvsXXrVqKiohg1ahSVlZWNnnPTpk1MmjSJKVOmsHv3bqZMmcLEiRPZvHkzJosfQ2+8g5SMAv7z5QpuHj2EeY/+nv7t/Lj+4xoyy10A7M13MvtHK0tuM7H4VhNPrrGyr8AJgN2pMOObOt65wYRGLXMWtHQGq4HHuj1Gv279PF2KEC2OhAHRItx4442MGzeOxMREEhMT+fvf/46fnx+//PILiqIwf/58nnrqKW699Va6devGokWLqKmpYfHixY2ec/78+YwaNYonnniCpKQknnjiCUaMGMH8+fMBiOuYzLZDOfTtEs/AXp2Jio7mhknTaBOo4e2tNgBSilz0iNRwXbyWER209IhUk1JYHxRe3mhjSFst/WI1zf7zEZdHZ9Nxb/S93Dj4Rk+XIkSLJGFAtDhOp5NPPvmE6upqBg4cSFpaGnl5eYwePdq9j8FgYOjQoWzcuLHR82zatKnBMQBjxoxxH2Oz2Th05CjXDR9GXuYxnE4HFZpQuiV1YmN2/af/7hFqDhc7ySx3kVHm4nCxi24RalJLXCzcZef562TgYUuns+m4w+8Ofjf2d6hU0oIjxNlIGBAtxt69e/Hz88NgMDBjxgyWLl1Kly5dyMvLAyAysmE/b2RkpHvb2eTl5Z3zmKKiIpxOJ8PH3UZMfAK5aUdQFAVNUBvSq00AJIdreGGEkVEf1TD6PzXMG2EkOVzDjOW1vDTKwPdHHXRbUEXvf1Xx02ljDYTn6Ww6blDdwB9u+oMMGBTiHLSeLkCIX3Xu3Jldu3ZRVlbGF198wdSpU1m3bp17++mf6hRFOe8nvQs5xi8wiOtu/i1fL3qDgpx0FEXBrjay0zyA3jXrmdFXz4y+J99IFu6y4W9QMbCNhs5vVbH1PgvZFQp3fF5L2p/8MGjl02dLoLPpGKcax6yJs/C3+Hu6HCFaNGkZEC2GXq+nU6dO9O3bl3nz5tGzZ0/+7//+j6ioKIAzWgEKCgrO+OR/qqioqHMeExYWhkajIS8vj7YJXbluwt2Aivz8fAL9LaSY+nPA2LfB8UU1Luaus/Lm9UY25zhJDFWTEKpheLwWuwsOF7ua4CchLpe+Ts8Nqht4ZOIjMsOgEBdAwoBosRRFwWq1Eh8fT1RUFKtWrXJvs9lsrFu3jkGDGp9OeODAgQ2OAVi5cqX7GL1eT58+fdz7dOkzmOE3T+bgsWziIkMA2GUZSqqhm/v4mSus/PlqA20C1DhdYD/lvd/hUnDKHYYeZ6mycIvmFmZNnCVBQIgLJN0EokV48sknuf7664mLi6OyspJPPvmEtWvXsmLFClQqFTNnzuSFF14gISGBhIQEXnjhBcxmM3fddZf7HHfffTexsbHMmzcPgD/96U8MGTKEF198kZtvvplly5axevVqfv75Z/cxs2bNYsqUKfTt25eBAwey8LP/UVljpU/nOCpKiwgIDmOrZRR6xcqhlBSOlDj5cIIRgP6xGg4WufjuiJ2sCgWNSkXnUMnXnhRUEsQ4/3HMmDhDgoAQF0HCgGgR8vPzmTJlCrm5uQQGBtKjRw9WrFjBqFGjAPjrX/9KbW0tDzzwAKWlpQwYMICVK1fi73+yLzgzMxO1+uSb8aBBg/jkk094+umneeaZZ+jYsSOffvopAwYMcO8zadIkiouLmTt3Lrm5uXTr1o1vv/sOdVU+G1d+iVqjxS8giLX6UfxtxV6+uM2E+sSYg9gANW9eb+SeZXUYtLDoFiMmnYwX8AgFovKjuK3tbdx1410E+AV4uiIhWhWVoijSsCnEaZwOBz8u+5ita78hIrYdJos/KsVJ/+pVdLSef+bDFi+kIzy8w/3t8wueJzsvm+DYYJYbl3uwsIundWppl9OOO6+6kwmjJ8hdA0JcAmkZEOIsNFotQ2+8E2tdDbs3/kBEm/aY/QLY7DeWSk0wPWt+RtoAPM9SayGpIInfjvwt1119XYOWISHEhZMwIEQjdHo9I2+bhkarZdeG1QSGhBMYGsEB0wAq1UEMrFqBFplbwBNUiorwgnB6unoy+bbJ9Onax9MlCdGqSRgQ4hwMRhOjbrsHv8AQfln1FVZrHeHRcWQZOlOjDmBI5VeYlBpPl+lTzHYz0RnRXNP+Gu4cfyexkbGeLkmIVk/a1IQ4D41Wy+AxtzJm4r2oVSqOpx/B5XJRrItmZeBdlGlCPV2iT9AoGmIKYuie3p3fDv4tf5z8RwkCQjQRaRkQ4gKoVCq6DxiKX2Awqz7/gKzUA8R26Ey1NpCVAXcyoHoV7WyHPF2m1wqrCyM0LZSkyCQmTplI98Tuss6AEE1IwoAQFyE+qQe33DOTlZ+9T0bqAWLaJ4DRxAb/8WRaE+lXvRqjUuvpMr2GxWkhIjuCaHs0wwYPY+zQsTJ/gBDNQMKAEBcpIrYdN98zk1Wf/z8O7d6MX2AoIRHRZBkSKdC1oW/1D7SzHfZ0ma2axWUhpigGU76JHok9uGXkLSTGJ0prgBDNRMKAEJfAPyiEG+9+iNj1ndmy5n9kHTlAVLuOYDCzwf9GMq2H6Ff9g7QSXCSzy0xcWRy6LB0x4TGMu20c1/a9VuYOEKKZSRgQ4hLp9AYGjLiRtgldWP/Npxw9sBP/wFCCI6LJMnSmQBdHv+ofaCutBOcV4gwhsjwSdbaa8MBwhowawrABwwgPCfd0aUL4BAkDQlym6LYdmTD9EXasX8nm01oJfva/kVhbKj1rfibIWezpUlsUjaKhraMtYSVhWPOsBAcGc83waxg2YBhR4VGeLk8InyJhQIgm8GsrQVynZNZ/8xnHUnbhHxhCcEQ0OfpOHNd1IN56gO61G7G4Kj1drucoEOYKI8YWgzHXSG1VLZZACyOGjWD41cOJiYjxdIVC+CQJA0I0oZh2nZgwfRY7fl7JtrXfknF4H8HhUQQEh3HM2I10QxKd6vbQpW4rZleVp8u9IlSKinBXOHHOOPzL/CkvKEelUhETE8M1I6/hqq5XSXeAEB4mYUCIJqY3GLl6xE0kdOvLzp9XsX/bejIP7yM0ug0W/yAOm64i1diTDtZ9dKndip+r3NMlNy0FApQAIlwRhDnCMFWYqCqpwmqz4vBz0L9nfwb1HkTXhK4Y9AZPVyuEQMKAEM0mNDKGkbdNpVv/a9m2bgVH9mylOC+HkIgY/AKDSTX25KihO21sqXSw7ifano4al6fLvjgKmBUzAUoAQa4gwl3hBNoCsVZaKS0vpcZeg85fR5dOXejdpTdJHZKIiYiRWwSFaGEkDAjRzKLiOnDD5D9wfPBIdm9aw+HdWyjJzyEoLBL/4DCyDIlkGRIxuGpobz1IvHU/Ic4CzxWsgAoVatTo0GFQDPUP6v9rVIz4KX4EugLxc/nhsrmoqKqgoqqCcls51ZpqAv0D6ZXUi15detE5vjORYZESAIRowVSKoiieLkIIX6EoCvnZaeze9CNH9myhsrwUrU5PcHgUJou/+w0z0FFEvHU/7a0pmJXqpi8kpCM8vMP97fMLnic7L5u2MW3PWrPT5cRut1NbV0tNXQ21dbXY7XYA9Do9Af4BxLeJJzE+kbbRbWkT1YZA/8Cmr1sI0SykZUCIK0ilUhEV14GouA4MHHkzaYf2cHDHJo5npFKQk47ZL5CgsEjKDWHs0g5lt/lawh05RNiziHDkEGY/3mzLJheVFlFTV4OiKCiKUh9MlPowoNFo0Ol0mAwmQgJDaNO5DTERMYQGhRIeEk5sVCwWk6VZ6hJCND9pGRDCw+pbC9I5lrKLgzs3UZSXjdPhwD8oFIt/IHqjyd1ioFKchDgKiHBkE27PJtxxHINSd/EvelrLwObdm0k5moJGrUGtVmM2mTEbzZhNZkxGE2Zj/X9DAkMI8AuQJn8hvIyEASFaELvNRvaxFFL37eDogZ1Ul5dhs9ai1mgx+wdg9gvEaLacfDNWFAKdRQQ4S7C4KrG4KjA7K7C4KjG7KjA2FhROCwNCCN8mYUCIFspmraMoN4vC3CyOZ6SSffQgleUlWGtqUKlVmCz+GExmdHojeoMRtUZzxid2jWLH4qzA5KpCg5OinDT6Dx1D26Te0Pl6D12ZEKKlkTAgRCvhsNspzs+hMDeLvMxjZKYeoKaqHLvVit1mxen8dSyBCq1Oh85gRKfXo1KpUalUqFQqSvKPc9PUh+nWf4hHr0UI0bJIGBCilVIUhdrqKmqqyqmpLKe6spyaygqqKkopKy6grLiAmqoKFJfrxKBAFxqtjhsm/4G2nbp4unwhRAsiYUAIL+Z0OHC5nLicTlyu+gmNTBY/D1clhGhpJAwIIYQQPk7t6QKEEEII4VkSBoQQQggfJ2FACCGE8HESBoQQQggfJ2FACCGE8HESBoQQQggfJ2FACCGE8HESBoQQQggfJ2FACCGE8HESBoQQQggfJ2FACCGE8HESBoQQQggfJ2FACCGE8HH/Hxu5Tz9fojY3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n",
    "sizes = [20, 30, 40, 10]\n",
    "explode = (0, 0.1, 0, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
    "\n",
    "fig1, (ax1, ax2) = plt.subplots(2)\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%', shadow=True)\n",
    "ax1.axis('equal')\n",
    "ax2.pie(sizes, autopct='%1.2f%%', shadow=True, startangle=90, explode=explode,\n",
    "    pctdistance=1.12)\n",
    "ax2.axis('equal')\n",
    "ax2.legend(labels=labels, loc='upper right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "饼图自动根据数据的百分比画饼.。labels是各个块的标签，如子图一。autopct=%1.1f%%表示格式化百分比精确输出，explode，突出某些块，不同的值突出的效果不一样。pctdistance=1.12百分比距离圆心的距离，默认是0.6."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1.6 箱形图\n",
    "为了专注于如何画图，省去数据的处理部分。 data 的 shape 为 (n, )， data2 的 shape 为 (n, 3)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x25f0f929640>,\n",
       "  <matplotlib.lines.Line2D at 0x25f0f929910>,\n",
       "  <matplotlib.lines.Line2D at 0x25f0f936a30>,\n",
       "  <matplotlib.lines.Line2D at 0x25f0f936d00>,\n",
       "  <matplotlib.lines.Line2D at 0x25f0f943e20>,\n",
       "  <matplotlib.lines.Line2D at 0x25f0f950130>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x25f0f929be0>,\n",
       "  <matplotlib.lines.Line2D at 0x25f0f929eb0>,\n",
       "  <matplotlib.lines.Line2D at 0x25f0f936fd0>,\n",
       "  <matplotlib.lines.Line2D at 0x25f0f9432e0>,\n",
       "  <matplotlib.lines.Line2D at 0x25f0f950400>,\n",
       "  <matplotlib.lines.Line2D at 0x25f0f9506d0>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x25f0f929370>,\n",
       "  <matplotlib.lines.Line2D at 0x25f0f936760>,\n",
       "  <matplotlib.lines.Line2D at 0x25f0f943b50>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x25f0f9361c0>,\n",
       "  <matplotlib.lines.Line2D at 0x25f0f9435b0>,\n",
       "  <matplotlib.lines.Line2D at 0x25f0f9509a0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x25f0f936490>,\n",
       "  <matplotlib.lines.Line2D at 0x25f0f943880>,\n",
       "  <matplotlib.lines.Line2D at 0x25f0f950c70>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2)\n",
    "data=np.arange(1,5,1)\n",
    "data2=np.arange(1,15,1)\n",
    "data2.resize(5,3)\n",
    "ax1.boxplot(data)\n",
    "ax2.boxplot(data2, vert=False) #控制方向"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1.7 泡泡图\n",
    "散点图的一种，加入了第三个值 s 可以理解成普通散点，画的是二维，泡泡图体现了Z的大小，如下例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(66190801)\n",
    "\n",
    "\n",
    "N = 50\n",
    "x = np.random.rand(N)\n",
    "y = np.random.rand(N)\n",
    "colors = np.random.rand(N)\n",
    "area = (30 * np.random.rand(N))**2  # 0 to 15 point radii\n",
    "\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1.8 等高线（轮廓图）\n",
    "有时候需要描绘边界的时候，就会用到轮廓图，机器学习用的决策边界也常用轮廓图来绘画，见下例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x18e0077aa60>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2)\n",
    "x = np.arange(-5, 5, 0.1)\n",
    "y = np.arange(-5, 5, 0.1)\n",
    "xx, yy = np.meshgrid(x, y, sparse=True)\n",
    "z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)\n",
    "ax1.contourf(x, y, z)\n",
    "ax2.contour(x, y, z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1.9 布局、图例说明、边界等\n",
    "### 1)区间上下限\n",
    "当绘画完成后，会发现X、Y轴的区间是会自动调整的，并不是跟我们传入的X、Y轴数据中的最值相同。为了调整区间我们使用下面的方式："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax.set_xlim([xmin, xmax])   #设置X轴的区间\n",
    "ax.set_ylim([ymin, ymax])   #Y轴区间\n",
    "ax.axis([xmin, xmax, ymin, ymax])   #X、Y轴区间\n",
    "ax.set_ylim(bottom=-10)     #Y轴下限\n",
    "ax.set_xlim(right=25)       #X轴上限b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "具体效果见下例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 2*np.pi)\n",
    "y = np.sin(x)\n",
    "fig, (ax1, ax2) = plt.subplots(2)\n",
    "ax1.plot(x, y)\n",
    "ax2.plot(x, y)\n",
    "ax2.set_xlim([-1, 6])\n",
    "ax2.set_ylim([-1, 3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) 图例说明\n",
    "我们如果我们在一个Axes上做多次绘画，那么可能出现分不清哪条线或点所代表的意思。这个时间添加图例说明，就可以解决这个问题了，见下例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot([1, 2, 3, 4], [10, 20, 25, 30], label='Philadelphia')\n",
    "ax.plot([1, 2, 3, 4], [30, 23, 13, 4], label='Boston')\n",
    "ax.scatter([1, 2, 3, 4], [20, 10, 30, 15], label='Point')\n",
    "ax.set(ylabel='Temperature (deg C)', xlabel='Time', title='A tale of two cities')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在绘图时传入 label 参数，并最后调用ax.legend()显示体力说明，对于 legend 还是传入参数，控制图例说明显示的位置：\n",
    "\n",
    "<table><thead><tr><th>Location String</th><th align=\"center\">Location Code</th></tr></thead><tbody><tr><td>‘best’</td><td align=\"center\">0</td></tr><tr><td>‘upper right’</td><td align=\"center\">1</td></tr><tr><td>‘upper left’</td><td align=\"center\">2</td></tr><tr><td>‘lower left’</td><td align=\"center\">3</td></tr><tr><td>‘lower right’</td><td align=\"center\">4</td></tr><tr><td>‘right’</td><td align=\"center\">5</td></tr><tr><td>‘center left’</td><td align=\"center\">6</td></tr><tr><td>‘center right’</td><td align=\"center\">7</td></tr><tr><td>‘lower center’</td><td align=\"center\">8</td></tr><tr><td>‘upper center’</td><td align=\"center\">9</td></tr><tr><td>‘center’</td><td align=\"center\">10</td></tr></tbody></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) 区间分段\n",
    "默认情况下，绘图结束之后，Axes 会自动的控制区间的分段。见下例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [('apples', 2), ('oranges', 3), ('peaches', 1)]\n",
    "fruit, value = zip(*data)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2)\n",
    "x = np.arange(len(fruit))\n",
    "ax1.bar(x, value, align='center', color='gray')\n",
    "ax2.bar(x, value, align='center', color='gray')\n",
    "\n",
    "ax2.set(xticks=x, xticklabels=fruit)\n",
    "\n",
    "#ax.tick_params(axis='y', direction='inout', length=10) #修改 ticks 的方向以及长度\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面不仅修改了X轴的区间段，并且修改了显示的信息为文本。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) 布局\n",
    "当我们绘画多个子图时，就会有一些美观的问题存在，例如子图之间的间隔，子图与画板的外边间距以及子图的内边距，下面说明这个问题："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 648x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(9, 9))\n",
    "fig.subplots_adjust(wspace=0.5, hspace=0.3,\n",
    "                    left=0.125, right=0.9,\n",
    "                    top=0.9,    bottom=0.1)\n",
    "\n",
    "#fig.tight_layout() #自动调整布局，使标题之间不重叠\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过fig.subplots_adjust()我们修改了子图水平之间的间隔wspace=0.5，垂直方向上的间距hspace=0.3，左边距left=0.125 等等，这里数值都是百分比的。以 [0, 1] 为区间，选择left、right、bottom、top 注意 top 和 right 是 0.9 表示上、右边距为百分之10。\n",
    "\n",
    "不确定如果调整的时候，fig.tight_layout()是一个很好的选择。之前说到了内边距，内边距是子图的，也就是 Axes 对象，所以这样使用 ax.margins(x=0.1, y=0.1)，当值传入一个值时，表示同时修改水平和垂直方向的内边距。\n",
    "\n",
    "观察上面的四个子图，可以发现他们的X、Y的区间是一致的，而且这样显示并不美观，所以可以调整使他们使用一样的X、Y轴：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True)\n",
    "ax1.plot([1, 2, 3, 4], [1, 2, 3, 4])\n",
    "ax2.plot([3, 4, 5, 6], [6, 5, 4, 3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) 轴相关\n",
    "改变边界的位置，去掉四周的边框："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot([-2, 2, 3, 4], [-10, 20, 25, 5])\n",
    "ax.spines['top'].set_visible(False)     #顶边界不可见\n",
    "ax.xaxis.set_ticks_position('bottom')  # ticks 的位置为下方，分上下的。\n",
    "ax.spines['right'].set_visible(False)   #右边界不可见\n",
    "ax.yaxis.set_ticks_position('left')  \n",
    "\n",
    "# \"outward\"\n",
    "# 移动左、下边界离 Axes 10 个距离\n",
    "#ax.spines['bottom'].set_position(('outward', 10))\n",
    "#ax.spines['left'].set_position(('outward', 10))\n",
    "\n",
    "# \"data\"\n",
    "# 移动左、下边界到 (0, 0) 处相交\n",
    "ax.spines['bottom'].set_position(('data', 0))\n",
    "ax.spines['left'].set_position(('data', 0))\n",
    "\n",
    "# \"axes\"\n",
    "# 移动边界，按 Axes 的百分比位置\n",
    "#ax.spines['bottom'].set_position(('axes', 0.75))\n",
    "#ax.spines['left'].set_position(('axes', 0.3))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = Section2> </a>\n",
    "## 7.2 Turtle\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Turtle库是Python语言中一个很流行的绘制图像的函数库，想象一个小乌龟，在一个横轴为x、纵轴为y的坐标系原点，(0,0)位置开始，它根据一组函数指令的控制，在这个平面坐标系中移动，从而在它爬行的路径上绘制了图形。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.2.1. 画布(canvas)\n",
    "\n",
    "画布就是turtle为我们展开用于绘图区域，我们可以设置它的大小和初始位置。\n",
    "\n",
    "设置画布大小<br>\n",
    "` turtle.screensize(canvwidth=None, canvheight=None, bg=None)`   \n",
    "参数分别为画布的宽(单位像素), 高, 背景颜色。\n",
    "\n",
    "如：`turtle.screensize(800,600, \"green\")`\n",
    "\n",
    "        turtle.screensize() #返回默认大小(400, 300)\n",
    "\n",
    "`turtle.setup(width=0.5, height=0.75, startx=None, starty=None)`<br>参数：width, height: 输入宽和高为整数时, 表示像素; 为小数时, 表示占据电脑屏幕的比例，(startx, starty): 这一坐标表示矩形窗口左上角顶点的位置, 如果为空,则窗口位于屏幕中心。\n",
    "\n",
    "如：turtle.setup(width=0.6,height=0.6)\n",
    "\n",
    "        turtle.setup(width=800,height=800, startx=100, starty=100)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.2.2. 画笔\n",
    "\n",
    "### 1) 画笔的状态\n",
    "\n",
    "        在画布上，默认有一个坐标原点为画布中心的坐标轴，坐标原点上有一只面朝x轴正方向小乌龟。这里我们描述小乌龟时使用了两个词语：坐标原点(位置)，面朝x轴正方向(方向)， turtle绘图中，就是使用位置方向描述小乌龟(画笔)的状态。\n",
    "\n",
    "### 2) 画笔的属性\n",
    "\n",
    "        画笔(画笔的属性，颜色、画线的宽度等)\n",
    "\n",
    "        1) turtle.pensize()：设置画笔的宽度；\n",
    "\n",
    "        2) turtle.pencolor()：没有参数传入，返回当前画笔颜色，传入参数设置画笔颜色，可以是字符串如\"green\", \"red\",也可以是RGB 3元组。\n",
    "\n",
    "        3) turtle.speed(speed)：设置画笔移动速度，画笔绘制的速度范围[0,10]整数，数字越大越快。\n",
    "\n",
    "### 3) 绘图命令\n",
    "\n",
    "         操纵海龟绘图有着许多的命令，这些命令可以划分为3种：一种为运动命令，一种为画笔控制命令，还有一种是全局控制命令。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(1)    画笔运动命令**\n",
    "<table border=\"1\" cellspacing=\"0\" cellpadding=\"0\"><tbody><tr><td> <p align=\"center\"><span style=\"font-size:14px\">命令</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">说明</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.forward(distance)</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">向当前画笔方向移动distance像素长度</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.backward(distance)</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">向当前画笔相反方向移动distance像素长度</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.right(degree)</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">顺时针移动degree°</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.left(degree)</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">逆时针移动degree°</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.pendown()</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">移动时绘制图形，缺省时也为绘制</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.goto(x,y)</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">将画笔移动到坐标为x,y的位置</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.penup()</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">提起笔移动，不绘制图形，用于另起一个地方绘制</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.circle()</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">画圆，半径为正(负)，表示圆心在画笔的左边(右边)画圆</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">setx( )</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">将当前x轴移动到指定位置</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">sety( )</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">将当前y轴移动到指定位置</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">setheading(angle)</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">设置当前朝向为angle角度</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">home()</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">设置当前画笔位置为原点，朝向东。</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">dot(r)</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">绘制一个指定直径和颜色的圆点</span></p> </td></tr></tbody></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(2)     画笔控制命令**\n",
    "<table border=\"1\" cellspacing=\"0\" cellpadding=\"0\"><tbody><tr><td> <p align=\"center\"><span style=\"font-size:14px\">命令</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">说明</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.fillcolor(colorstring)</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">绘制图形的填充颜色</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.color(color1, color2)</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">同时设置pencolor=color1, fillcolor=color2</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.filling()</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">返回当前是否在填充状态</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.begin_fill()</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">准备开始填充图形</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.end_fill()</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">填充完成</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.hideturtle()</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">隐藏画笔的turtle形状</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.showturtle()</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">显示画笔的turtle形状</span></p> </td></tr></tbody></table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(3)    全局控制命令**\n",
    "<table border=\"1\" cellspacing=\"0\" cellpadding=\"0\"><tbody><tr><td> <p align=\"center\"><span style=\"font-size:14px\">命令</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">说明</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.clear()</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">清空turtle窗口，但是turtle的位置和状态不会改变</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.reset()</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">清空窗口，重置turtle状态为起始状态</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.undo()</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">撤销上一个turtle动作</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.isvisible()</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">返回当前turtle是否可见</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">stamp()</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">复制当前图形</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.write(s [,font=(\"font-name\",font_size,\"font_type\")])</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">写文本，s为文本内容，font是字体的参数，分别为字体名称，大小和类型；font为可选项，font参数也是可选项</span></p> </td></tr></tbody></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(4)    其他命令**\n",
    "<table border=\"1\" cellspacing=\"0\" cellpadding=\"0\"><tbody><tr><td> <p align=\"center\"><span style=\"font-size:14px\">命令</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\">说明</span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.mainloop()或turtle.done()</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\"><span style=\"color:rgb(34,34,34)\">启动事件循环</span><span style=\"color:rgb(34,34,34)\"> -</span><span style=\"color:rgb(34,34,34)\">调用</span><span style=\"color:rgb(34,34,34)\">Tkinter</span><span style=\"color:rgb(34,34,34)\">的</span><span style=\"color:rgb(34,34,34)\">mainloop</span><span style=\"color:rgb(34,34,34)\">函数。</span></span></p> <p align=\"center\"><span style=\"color:rgb(34,34,34)\"><span style=\"font-size:14px\">必须是乌龟图形程序中的最后一个语句。</span></span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.mode(mode=None)</span></p> </td><td> <p align=\"center\"><span style=\"font-size:14px\"><span style=\"color:rgb(34,34,34)\">设置乌龟模式（</span><span style=\"color:rgb(34,34,34)\">“standard”</span><span style=\"color:rgb(34,34,34)\">，</span><span style=\"color:rgb(34,34,34)\">“logo”</span><span style=\"color:rgb(34,34,34)\">或</span><span style=\"color:rgb(34,34,34)\">“world”</span><span style=\"color:rgb(34,34,34)\">）并执行重置。如果没有给出模式，则返回当前模式。</span></span></p> \n",
    "     <div align=\"center\"> \n",
    "      <div class=\"table-box\"><table border=\"1\" cellspacing=\"0\" cellpadding=\"0\"><tbody><tr><td> <p align=\"center\"><span style=\"color:#222222\"><span style=\"font-size:14px\">模式</span></span></p> </td><td> <p align=\"center\"><span style=\"color:#222222\"><span style=\"font-size:14px\">初始龟标题</span></span></p> </td><td> <p align=\"center\"><span style=\"color:#222222\"><span style=\"font-size:14px\">正角度</span></span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"color:rgb(34,34,34)\"><span style=\"font-size:14px\">standard</span></span></p> </td><td> <p align=\"center\"><span style=\"color:#222222\"><span style=\"font-size:14px\">向右（东）</span></span></p> </td><td> <p align=\"center\"><span style=\"color:#222222\"><span style=\"font-size:14px\">逆时针</span></span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"color:rgb(34,34,34)\"><span style=\"font-size:14px\">logo</span></span></p> </td><td> <p align=\"center\"><span style=\"color:#222222\"><span style=\"font-size:14px\">向上（北）</span></span></p> </td><td> <p align=\"center\"><span style=\"color:#222222\"><span style=\"font-size:14px\">顺时针</span></span></p> </td></tr></tbody></table></div> \n",
    "     </div> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.delay(delay=None)</span></p> </td><td> <p align=\"center\"><span style=\"color:rgb(34,34,34)\"><span style=\"font-size:14px\">设置或返回以毫秒为单位的绘图延迟。</span></span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.begin_poly()</span></p> </td><td> <p align=\"center\"><span style=\"color:rgb(34,34,34)\"><span style=\"font-size:14px\">开始记录多边形的顶点。当前的乌龟位置是多边形的第一个顶点。</span></span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.end_poly()</span></p> </td><td> <p align=\"center\"><span style=\"color:rgb(34,34,34)\"><span style=\"font-size:14px\">停止记录多边形的顶点。当前的乌龟位置是多边形的最后一个顶点。将与第一个顶点相连。</span></span></p> </td></tr><tr><td> <p align=\"center\"><span style=\"font-size:14px\">turtle.get_poly()</span></p> </td><td> <p align=\"center\"><span style=\"color:rgb(34,34,34)\"><span style=\"font-size:14px\">返回最后记录的多边形。</span></span></p> </td></tr></tbody></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.2.3. 命令详解\n",
    "\n",
    "        1) turtle.circle(radius, extent=None, steps=None)\n",
    "\n",
    "        描述：以给定半径画圆\n",
    "\n",
    "        参数：\n",
    "\n",
    "        radius(半径)：半径为正(负)，表示圆心在画笔的左边(右边)画圆；\n",
    "\n",
    "        extent(弧度) (optional)；\n",
    "\n",
    "        steps (optional) (做半径为radius的圆的内切正多边形，多边形边数为steps)。\n",
    "\n",
    "举例:\n",
    "circle(50) # 整圆;\n",
    "\n",
    "circle(50,steps=3) # 三角形;\n",
    "\n",
    "circle(120, 180) # 半圆"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**实例：**\n",
    "\n",
    "1) 太阳花"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coding=utf-8\n",
    "import turtle\n",
    "import time\n",
    " \n",
    "# 同时设置pencolor=color1, fillcolor=color2\n",
    "turtle.color(\"red\", \"yellow\")\n",
    " \n",
    "turtle.begin_fill()\n",
    "for _ in range(50):\n",
    "    turtle.forward(200)\n",
    "    turtle.left(170)\n",
    "    turtle.end_fill()\n",
    "    \n",
    "turtle.mainloop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) 五角星"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coding=utf-8\n",
    "import turtle\n",
    "import time\n",
    " \n",
    "turtle.pensize(5)\n",
    "turtle.pencolor(\"yellow\")\n",
    "turtle.fillcolor(\"red\")\n",
    " \n",
    "turtle.begin_fill()\n",
    "for _ in range(5):\n",
    "  turtle.forward(200)\n",
    "  turtle.right(144)\n",
    "turtle.end_fill()\n",
    "time.sleep(2)\n",
    "\n",
    "turtle.penup()\n",
    "turtle.goto(-150,-120)\n",
    "turtle.color(\"violet\")\n",
    "turtle.write(\"Done\", font=('Arial', 40, 'normal'))\n",
    " \n",
    "turtle.mainloop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) 时钟程序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coding=utf-8\n",
    " \n",
    "import turtle\n",
    "from datetime import *\n",
    " \n",
    "# 抬起画笔，向前运动一段距离放下\n",
    "def Skip(step):\n",
    "    turtle.penup()\n",
    "    turtle.forward(step)\n",
    "    turtle.pendown()\n",
    " \n",
    "def mkHand(name, length):\n",
    "    # 注册Turtle形状，建立表针Turtle\n",
    "    turtle.reset()\n",
    "    Skip(-length * 0.1)\n",
    "    # 开始记录多边形的顶点。当前的乌龟位置是多边形的第一个顶点。\n",
    "    turtle.begin_poly()\n",
    "    turtle.forward(length * 1.1)\n",
    "    # 停止记录多边形的顶点。当前的乌龟位置是多边形的最后一个顶点。将与第一个顶点相连。\n",
    "    turtle.end_poly()\n",
    "    # 返回最后记录的多边形。\n",
    "    handForm = turtle.get_poly()\n",
    "    turtle.register_shape(name, handForm)\n",
    " \n",
    "def Init():\n",
    "    global secHand, minHand, hurHand, printer\n",
    "    # 重置Turtle指向北\n",
    "    turtle.mode(\"logo\")\n",
    "    # 建立三个表针Turtle并初始化\n",
    "    mkHand(\"secHand\", 135)\n",
    "    mkHand(\"minHand\", 125)\n",
    "    mkHand(\"hurHand\", 90)\n",
    "    secHand = turtle.Turtle()\n",
    "    secHand.shape(\"secHand\")\n",
    "    minHand = turtle.Turtle()\n",
    "    minHand.shape(\"minHand\")\n",
    "    hurHand = turtle.Turtle()\n",
    "    hurHand.shape(\"hurHand\")\n",
    "   \n",
    "    for hand in secHand, minHand, hurHand:\n",
    "        hand.shapesize(1, 1, 3)\n",
    "        hand.speed(0)\n",
    "   \n",
    "    # 建立输出文字Turtle\n",
    "    printer = turtle.Turtle()\n",
    "    # 隐藏画笔的turtle形状\n",
    "    printer.hideturtle()\n",
    "    printer.penup()\n",
    "    \n",
    "def SetupClock(radius):\n",
    "    # 建立表的外框\n",
    "    turtle.reset()\n",
    "    turtle.pensize(7)\n",
    "    for i in range(60):\n",
    "        Skip(radius)\n",
    "        if i % 5 == 0:\n",
    "            turtle.forward(20)\n",
    "            Skip(-radius - 20)\n",
    "           \n",
    "            Skip(radius + 20)\n",
    "            if i == 0:\n",
    "                turtle.write(int(12), align=\"center\", font=(\"Courier\", 14, \"bold\"))\n",
    "            elif i == 30:\n",
    "                Skip(25)\n",
    "                turtle.write(int(i/5), align=\"center\", font=(\"Courier\", 14, \"bold\"))\n",
    "                Skip(-25)\n",
    "            elif (i == 25 or i == 35):\n",
    "                Skip(20)\n",
    "                turtle.write(int(i/5), align=\"center\", font=(\"Courier\", 14, \"bold\"))\n",
    "                Skip(-20)\n",
    "            else:\n",
    "                turtle.write(int(i/5), align=\"center\", font=(\"Courier\", 14, \"bold\"))\n",
    "            Skip(-radius - 20)\n",
    "        else:\n",
    "            turtle.dot(5)\n",
    "            Skip(-radius)\n",
    "        turtle.right(6)\n",
    "        \n",
    "def Week(t):   \n",
    "    week = [\"星期一\", \"星期二\", \"星期三\",\n",
    "            \"星期四\", \"星期五\", \"星期六\", \"星期日\"]\n",
    "    return week[t.weekday()]\n",
    " \n",
    "def Date(t):\n",
    "    y = t.year\n",
    "    m = t.month\n",
    "    d = t.day\n",
    "    return \"%s %d%d\" % (y, m, d)\n",
    " \n",
    "def Tick():\n",
    "    # 绘制表针的动态显示\n",
    "    t = datetime.today()\n",
    "    second = t.second + t.microsecond * 0.000001\n",
    "    minute = t.minute + second / 60.0\n",
    "    hour = t.hour + minute / 60.0\n",
    "    secHand.setheading(6 * second)\n",
    "    minHand.setheading(6 * minute)\n",
    "    hurHand.setheading(30 * hour)\n",
    "    \n",
    "    turtle.tracer(False) \n",
    "    printer.forward(65)\n",
    "    printer.write(Week(t), align=\"center\",\n",
    "                  font=(\"Courier\", 14, \"bold\"))\n",
    "    printer.back(130)\n",
    "    printer.write(Date(t), align=\"center\",\n",
    "                  font=(\"Courier\", 14, \"bold\"))\n",
    "    printer.home()\n",
    "    turtle.tracer(True)\n",
    " \n",
    "    # 100ms后继续调用tick\n",
    "    turtle.ontimer(Tick, 100)\n",
    " \n",
    "def main():\n",
    "    # 打开/关闭龟动画，并为更新图纸设置延迟。\n",
    "    turtle.tracer(False)\n",
    "    Init()\n",
    "    SetupClock(160)\n",
    "    turtle.tracer(True)\n",
    "    Tick()\n",
    "    turtle.mainloop()\n",
    " \n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = Section3> </a>\n",
    "## 7.3 PIL\n",
    "PIL(Python Image Library)是python的第三方图像处理库，但是由于其强大的功能与众多的使用人数，几乎已经被认为是python官方图像处理库了。<br>\n",
    "其官方主页为:[PIL](http://pythonware.com/products/pil/)。<br>\n",
    "PIL历史悠久，原来是只支持python2.x的版本的，后来出现了移植到python3的库[pillow](http://python-pillow.org/),pillow号称是`friendly fork for PIL`,其功能和PIL差不多，但是支持python3。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PIL可以做很多和图像处理相关的事情: \n",
    "- **图像归档(Image Archives)**。PIL非常适合于图像归档以及图像的批处理任务。你可以使用PIL创建缩略图，转换图像格式，打印图像等等。 \n",
    "- **图像展示(Image Display)**。PIL较新的版本支持包括Tk PhotoImage，BitmapImage还有Windows DIB等接口。PIL支持众多的GUI框架接口，可以用于图像展示。 \n",
    "- **图像处理(Image Processing)**。PIL包括了基础的图像处理函数，包括对点的处理，使用众多的卷积核(convolution kernels)做过滤(filter),还有颜色空间的转换。\n",
    "PIL库同样支持图像的大小转换，图像旋转，以及任意的仿射变换。PIL还有一些直方图的方法，允许你展示图像的一些统计特性。这个可以用来实现图像的自动对比度增强，还有全局的统计分析等。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.3.1 Image class\n",
    "###  Image类是PIL中的核心类，你有很多种方式来对它进行初始化，比如从文件中加载一张图像，处理其他形式的图像，或者是从头创造一张图像等。 \n",
    "下面是PIL Image类中常用的方法: <br> \n",
    "- **open(filename,mode)**(打开一张图像)。下面的代码演示了如何从文件打开一张图像: <br> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image \n",
    "#Image.open(\"dog.jpg\",\"r\") \n",
    "im = Image.open(\"./img/dog.jfif\",\"r\") \n",
    "print(im.size,im.format,im.mode) \n",
    "#(296, 299) JPEG RGB\n",
    "im.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "help(Image.open)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Image.open`返回一个Image对象，该对象有`size,format,mode`等属性，其中`size`表示图像的宽度和高度(像素表示);`format`表示图像的格式,常见的包括JPEG,PNG等格式;`mode`表示图像的模式，定义了像素类型还有图像深度等，常见的有RGB,HSV等。一般来说'L'(luminance)表示灰度图像,'RGB'表示真彩图像,'CMYK'表示预先压缩的图像。一旦你得到了打开的Image对象之后，就可以使用其众多的方法对图像进行处理了，比如使用`im.show()`可以展示上面得到的图像。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ **save(filename,format)(保存指定格式的图像)**<br>\n",
    "`im.save(\"dog.png\",'png')`<br>\n",
    "上面的代码将图像重新保存成png格式。<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "+ **thumbnail(size,resample)(创建缩略图)**<br>\n",
    "`im.thumbnail((50,50),resample=Image.BICUBIC)`<br>\n",
    "`im.show()`<br>\n",
    "上面的代码可以创建一个指定大小(size)的缩略图,需要注意的是，`thumbnail`方法是原地操作，返回值是None。第一个参数是指定的缩略图的大小，第二个是采样的，有`Image.BICUBIC`，`PIL.Image.LANCZOS`，`PIL.Image.BILINEAR`，`PIL.Image.NEAREST`这四种采样方法。默认是`Image.BICUBIC`。<br>\n",
    "\n",
    "+ **crop(box)(裁剪矩形区域)**<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#im = Image.open(\"dog.jpg\",\"r\")\n",
    "box = (100,100,200,200)\n",
    "region = im.crop(box)\n",
    "region.show()\n",
    "im.crop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的代码在im图像上裁剪了一个box矩形区域，然后显示出来。box是一个有四个数字的元组`(upper_left_x,upper_left_y,lower_right_x,lower_right_y)`,分别表示裁剪矩形区域的左上角x,y坐标,右下角的x,y坐标,规定图像的最左上角的坐标为原点`(0,0)`,宽度的方向为x轴，高度的方向为y轴，每一个像素代表一个坐标单位。`crop()`返回的仍然是一个Image对象。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ **transpose(method)(图像翻转或者旋转)**<br>\n",
    "`im_rotate_180 = im.transpose(Image.ROTATE_180)`<br>\n",
    "`im_rotate_180.show()`  \n",
    "上面的代码将im逆时针旋转180°，然后显示出来,`method`是transpose的参数，表示选择什么样的翻转或者旋转方式，可以选择的值有:\n",
    "    - `Image.FLIP_LEFT_RIGHT`,表示将图像左右翻转\n",
    "    - `Image.FLIP_TOP_BOTTOM`,表示将图像上下翻转\n",
    "    - `Image.ROTATE_90`,表示将图像逆时针旋转90°\n",
    "    - `Image.ROTATE_180`,表示将图像逆时针旋转180°\n",
    "    - `Image.ROTATE_270`,表示将图像逆时针旋转270°\n",
    "    - `Image.TRANSPOSE`,表示将图像进行转置(相当于顺时针旋转90°)\n",
    "    - `Image.TRANSVERSE`,表示将图像进行转置,再水平翻转"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "im_rotate_180 = im.transpose(Image.ROTATE_180)\n",
    "im_rotate_180.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ **paste(region,box,mask)(将一个图像粘贴到另一个图像)**  \n",
    "`>>> im.paste(region,(100,100),None)`  \n",
    "`>>> im.show()`  \n",
    "上面的代码将region图像粘贴到左上角为`(100,100)`的位置。region是要粘贴的Image对象,box是要粘贴的位置，可以是一个两个元素的元组，表示粘贴区域的左上角坐标,也可以是一个四个元素的元组，表示左上角和右下角的坐标。如果是四个元素元组的话,box的size必须要和region的size保持一致，否则将会被convert成和region一样的size。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ **split()(颜色通道分离)**  \n",
    "  \n",
    "split()方法可以原来图像的各个通道分离,比如对于RGB图像，可以将其R,G,B三个颜色通道分离。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "r,g,b = im.split()  \n",
    "r.show()  \n",
    "g.show()  \n",
    "b.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ **merge(mode,channels)(颜色通道合并)**  \n",
    "  \n",
    "merge方法和split方法是相对的，其将多个单一通道的序列合并起来，组成一个多通道的图像，mode是合并之后图像的模式，比如\"RGB\",channels是多个单一通道组成的序列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "im_merge = Image.merge(\"RGB\",[b,r,g])  \n",
    "im_merge.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ **resize(size,resample,box)**  \n",
    "\n",
    "resize方法可以将原始的图像转换大小,size是转换之后的大小,resample是重新采样使用的方法，仍然有`Image.BICUBIC`，`PIL.Image.LANCZOS`，`PIL.Image.BILINEAR`，`PIL.Image.NEAREST`这四种采样方法，默认是`PIL.Image.NEAREST`,box是指定的要resize的图像区域，是一个用四个元组指定的区域(含义和上面所述box一致)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "im_resize = im.resize((200,200))  \n",
    "im_resize \n",
    "#<PIL.Image.Image image mode=RGB size=200x200 at 0x7F62B9E23470>  \n",
    "im_resize.show()  \n",
    "im_resize_box = im.resize((100,100),box = (0,0,50,50))  \n",
    "im_resize_box.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ **convert(mode,matrix,dither,palette,colors)(mode转换)**  \n",
    "  \n",
    "convert方法可以改变图像的mode,一般是在'RGB'(真彩图)、'L'(灰度图)、'CMYK'(压缩图)之间转换。上面的代码就是首先将图像转化为灰度图，再从灰度图转化为真彩图。值得注意的是,从灰度图转换为真彩图，虽然理论上确实转换成功了，但是实际上是很难恢复成原来的真彩模式的(不唯一)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'im' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_16632\\994849822.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mim_L\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"L\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mim_L\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mim_rgb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mim_L\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"RGB\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mim_rgb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mim_L\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'im' is not defined"
     ]
    }
   ],
   "source": [
    "im_L = im.convert(\"L\")  \n",
    "im_L.show()  \n",
    "im_rgb = im_L.convert(\"RGB\")  \n",
    "im_rgb.show() \n",
    "im_L.mode  \n",
    "#'L'  \n",
    "im_rgb.mode \n",
    "#'RGB'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ **filter(filter)(应用过滤器)**  \n",
    " \n",
    "filter方法可以将一些过滤器操作应用于原始图像，比如模糊操作，查找边、角点操作等。filter是过滤器函数，在PIL.ImageFilter函数中定义了大量内置的filter函数，比如BLUR(模糊操作),`GaussianBlur`(高斯模糊),`MedianFilter`(中值过滤器)，`FIND_EDGES`(查找边)等。上面得到原始图像`dog.jpg`,`find_edges.jpg`以及`blur.jpg`\n",
    "从左到右如下图1所示:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#im = Image.open(\"dog.jpg\",\"r\")  \n",
    "from PIL import ImageFilter  \n",
    "im_blur = im.filter(ImageFilter.BLUR)  \n",
    "im_blur.show()  \n",
    "im_find_edges = im.filter(ImageFilter.FIND_EDGES)  \n",
    "im_find_edges.show()  \n",
    "im_find_edges.save(\"find_edges.jpg\")  \n",
    "im_blur.save(\"blur.jpg\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ **point(lut,mode)(对图像像素操作)**  \n",
    "point方法可以对图像进行单个像素的操作，上面的代码对point方法传入了一个匿名函数,表示将图像的每个像素点大小都乘以1.5,mode是返回的图像的模式，默认是和原来图像的mode是一样的。图2是原来的dog.jpg和point操作之后的im_point.jpg之间的对比。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "im_point = im.point(lambda x:x*1.5)\n",
    "im_point.show()\n",
    "im_point.save(\"im_point.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面是一个结合了`point`函数,`split`函数,`paste`函数以及`merge`函数的小例子。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'im' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_12484\\2087349502.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msource\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mR\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mG\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mB\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msource\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mR\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpoint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m<\u001b[0m\u001b[1;36m100\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;36m255\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# x<100,return 255,otherwise return 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mout_G\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msource\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mG\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpoint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m0.7\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'im' is not defined"
     ]
    }
   ],
   "source": [
    "source = im.split() \n",
    "R,G,B = 0,1,2 \n",
    "mask = source[R].point(lambda x: x<100 and 255) \n",
    "# x<100,return 255,otherwise return 0 \n",
    "out_G = source[G].point(lambda x:x*0.7) \n",
    "# 将out_G粘贴回来，但是只保留'R'通道像素值<100的部分 \n",
    "source[G].paste(out_G,None,mask) \n",
    "# 合并成新的图像 \n",
    "im_new = Image.merge(im.mode,source) \n",
    "im_new.show() \n",
    "im.show() \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **ImageEnhance()**(图像增强) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import ImageEnhance \n",
    "brightness = ImageEnhance.Brightness(im) \n",
    "im_brightness = brightness.enhance(1.5) \n",
    "im_brightness.show() \n",
    "im_contrast = ImageEnhance.Contrast(im) \n",
    "im_contrast.enhance(1.5) \n",
    "im_contrast.enhance(1.5).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ImageEnhance是PIL下的一个子类，主要用于图像增强，比如增加亮度(Brightness),增加对比度(Contrast)等。上面的代码将原来图像的亮度增加50%,将对比度也增加了50%。 \n",
    "- **ImageSequence()**(处理图像序列) \n",
    "下面的代码可以遍历gif图像中的所有帧，并分别保存为图像 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import ImageSequence \n",
    "from PIL import Image \n",
    "gif = Image.open(\"pipixia.gif\") \n",
    "for i,frame in enumerate(ImageSequence.Iterator(gif),1): \n",
    "    if frame.mode == 'JPEG': \n",
    "        frame.save(\"%d.jpg\" %i) \n",
    "    else: \n",
    "        frame.save(\"%d.png\" % i) \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "除了上面使用迭代器的方式以外，还可以一帧一帧读取gif,比如下面的代码: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = 0 \n",
    "while 1: \n",
    "    try: \n",
    "        gif.seek(index) \n",
    "        gif.save(\"%d.%s\" %(index,'jpg' if gif.mode == 'JPEG' else 'png'))\n",
    "        index += 1 \n",
    "    except EOFError: \n",
    "        print(\"Reach the end of gif sequence!\") \n",
    "        break  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "上面的代码在读取到gif的最后一帧之后，会throw 一个 EOFError,所以我们只要捕获这个异常就可以了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 图片浮雕处理+文字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image, ImageFilter,ImageFont, ImageDraw\n",
    "im=Image.open(\"./img/library.png\")\n",
    "outimag=im.filter(ImageFilter.EMBOSS)\n",
    "ft = ImageFont.truetype(\"C:/WINDOWS/Fonts/HGPTY_CNKI.TTF\", 100)\n",
    "draw=ImageDraw.Draw(outimag)\n",
    "draw.text((30,400), u\"Python图像处理库PIL从入门到精通\",font = ft, fill = 'yellow')\n",
    "outimag.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python解析Raw格式图像\n",
    "\n",
    "简记下使用rawpy提取raw图像并转化为rgb。rawpy就是libraw的python封装，rawpy.imread直接可以读到raw数据，postprocess方法可以走完isp（BWC、RBGain、demosaic、gamma），得到一副RGB图像。postprocess的参数可以用来控制后处理流程，raw还有enhance模块，主要封装了坏点矫正，更细致的修改查阅官方文档。\n",
    "\n",
    "postprocess不加参数，图片颜色可能不正常，use_camera_wb（如果raw中有相机拍摄时的白平衡参数）或use_auto_wb（使用自带的白平衡算法）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://github.com/letmaik/rawpy\n",
    "\n",
    "import rawpy\n",
    "import imageio\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "raw = rawpy.imread('./img/花.NEF')\n",
    "\n",
    "rgb = raw.postprocess(use_camera_wb=True, half_size=True, \n",
    "no_auto_bright=True, output_bps=16)\n",
    "raw.close()\n",
    "\n",
    "print(rgb.dtype, rgb.shape)\n",
    "imageio.imsave('image.tif', rgb)\n",
    "plt.imshow(rgb)\n",
    "plt.pause(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rawpy\n",
    " \n",
    "openpath = \"D:\\\\IMG\\\\RAWSample\\\\Img.ARW\";\n",
    "savepath = 'D:\\\\IMG\\\\RAWSample\\\\Bayer.raw';\n",
    " \n",
    "with rawpy.imread(openpath) as raw:\n",
    "    bayer_visible = raw.raw_image_visible;\n",
    "    width = bayer_visible.shape[0];\n",
    "    height = bayer_visible.shape[1];\n",
    "#将读取到的RAW原始数据打印出来，\n",
    "#可以知道原始数据图像的宽和高，\n",
    "#打印出原始数据的二维矩阵可以知道图像的位深度。\n",
    "print(bayer_visible.shape)\n",
    "print(bayer_visible)\n",
    "\n",
    "#将二维整型数组转换为字节流，\n",
    "#每两个字节存储一个整型数据，低位在前，高位在后。\n",
    "\n",
    "with open(savepath, 'wb') as f:\n",
    "    for x in range(0,width):\n",
    "        for y in range(0,height):\n",
    "            data = int(bayer_visible[x][y]);\n",
    "            f.write(data.to_bytes(2, byteorder='little'));\n",
    "\n",
    "#得到的RAW文件格式为：\n",
    "# 宽度6024，高度4024，深度：12位，\n",
    "# 颜色滤波阵列为RGGB。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = Section4> </a>\n",
    "## 7.4 PyVista简介\n",
    "VTK 是一套功能强大的绘图工具，而在 Python 中亦可直接使用 VTK 进行各种图形的绘制，但是 Python 的 VTK 使用方式几乎跟 C++ 版本相同，在开发上需要撰写大量的代码，而 PyVista 则是以 VTK 为基础所开发的 API，让用户可以更方便使用 VTK 进行绘图。<br>\n",
    "其官方主页为:[PyVista](https://docs.pyvista.org/)。<br>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n"
     ]
    }
   ],
   "source": [
    "!pip list |grep \n",
    "pyvista"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Maps and Geoscience\n",
    "Download the surface elevation map of Mount St. Helens and plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyvista import examples\n",
    "mesh = examples.download_st_helens()\n",
    "warped = mesh.warp_by_scalar('Elevation')\n",
    "surf = warped.extract_surface().triangulate()\n",
    "surf = surf.decimate_pro(0.75)  # reduce the density of the mesh by 75%\n",
    "surf.plot(cmap='gist_earth')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finite Element Analysis \n",
    "Plot the ‘X’ component of elastic stress of a 3D notch specimen\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyvista import examples\n",
    "mesh = examples.download_notch_stress()\n",
    "mesh.plot(scalars='Nodal Stress', component=0, cmap='turbo', cpos='xy')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Point Cloud with Numpy\n",
    "Easily integrate with NumPy and create a variety of geometries and plot them. You could use any geometry to create your glyphs, or even plot the points directly.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pyvista\n",
    "\n",
    "point_cloud = np.random.random((100, 3))\n",
    "pdata = pyvista.PolyData(point_cloud)\n",
    "pdata['orig_sphere'] = np.arange(100)\n",
    "\n",
    "# create many spheres from the point cloud\n",
    "sphere = pyvista.Sphere(radius=0.02, phi_resolution=10, theta_resolution=10)\n",
    "pc = pdata.glyph(scale=False, geom=sphere, orient=False)\n",
    "pc.plot(cmap='Reds')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot a Spline \n",
    "Generate a spline from an array of NumPy points.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pyvista\n",
    "\n",
    "# Make the xyz points\n",
    "theta = np.linspace(-10 * np.pi, 10 * np.pi, 100)\n",
    "z = np.linspace(-2, 2, 100)\n",
    "r = z**2 + 1\n",
    "x = r * np.sin(theta)\n",
    "y = r * np.cos(theta)\n",
    "points = np.column_stack((x, y, z))\n",
    "\n",
    "spline = pyvista.Spline(points, 500).tube(radius=0.1)\n",
    "spline.plot(scalars='arc_length', show_scalar_bar=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boolean Operations on Meshes \n",
    "Subtract a sphere from a cube mesh."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyvista\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def make_cube():\n",
    "    x = np.linspace(-0.5, 0.5, 25)\n",
    "    grid = pyvista.StructuredGrid(*np.meshgrid(x, x, x))\n",
    "    surf = grid.extract_surface().triangulate()\n",
    "    surf.flip_normals()\n",
    "    return surf\n",
    "\n",
    "\n",
    "# Create example PolyData meshes for boolean operations\n",
    "sphere = pyvista.Sphere(radius=0.65, center=(0, 0, 0))\n",
    "cube = make_cube()\n",
    "\n",
    "# Perform a boolean difference\n",
    "boolean = cube.boolean_difference(sphere)\n",
    "boolean.plot(color='darkgrey', smooth_shading=True, split_sharp_edges=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demo Using pythreejs\n",
    "Create interactive physically based rendering using pythreejs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyvista import examples\n",
    "\n",
    "# download an example and display it using physically based rendering.\n",
    "#mesh = examples.download_lucy()\n",
    "import pyvista as pv\n",
    "mesh = pv.read(\"./objfile/Buddha.obj\")\n",
    "mesh.plot(color='lightgrey', pbr=True, metallic=0.2,\n",
    "          jupyter_backend='pythreejs')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demo Using ipygany\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyvista import demos\n",
    "\n",
    "# basic glyphs demo\n",
    "mesh = demos.glyphs(2)\n",
    "\n",
    "text = demos.logo.text_3d(\"I'm interactive!\", depth=0.2)\n",
    "text.points *= 0.1\n",
    "text.translate([0, 1.4, 1.5], inplace=True)\n",
    "mesh += text\n",
    "mesh['Example Scalars'] = mesh.points[:, 0]\n",
    "\n",
    "mesh.plot(cpos='xy', jupyter_backend='ipygany', show_scalar_bar=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demo Using panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyvista import demos\n",
    "demos.plot_logo(jupyter_backend='panel')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c9c8cb561baf4fa891a8b7c65c11ca92",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Scene(background_color='#4c4c4c', camera={'position': [0.8948567246598957, 0.8948567246598957, 0.8948567246598…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import keyboard\n",
    "from re import S\n",
    "import pyvista as pv\n",
    "mesh=pv.read(\"./objfile/兔子.obj\")\n",
    "#pv.set_jupyter_backend('static')\n",
    "#mesh.plot()\n",
    "\n",
    "plotter = pv.Plotter(notebook=True)\n",
    "plotter.add_mesh(mesh)\n",
    "plotter.show(jupyter_backend='ipygany')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(10):\n",
    "    b\n",
    "    t = i+1\n",
    "    item = './贴图/%d.png' % t\n",
    "    print(item)\n",
    "    texture = pv.read_texture(item)\n",
    "    mesh.plot(lighting=True, texture=texture)\n",
    "    keyboard.wait(\"esc\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里是一个官方提供的弹出式窗口的绘图方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\Anaconda3\\envs\\python310\\lib\\site-packages\\pyvista\\core\\dataset.py:1474: PyVistaDeprecationWarning: Use of `point_arrays` is deprecated. Use `point_data` instead.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from threading import Thread\n",
    "import time\n",
    "import numpy as np\n",
    "import pyvista as pv\n",
    "import pyvistaqt as pvqt\n",
    "from pyvista import examples\n",
    "\n",
    "\n",
    "globe = examples.load_globe()\n",
    "globe.point_arrays['scalars'] = np.random.rand(globe.n_points)\n",
    "globe.set_active_scalars('scalars')\n",
    "\n",
    "\n",
    "plotter = pvqt.BackgroundPlotter()\n",
    "plotter.add_mesh(globe, lighting=False, show_edges=True,\n",
    "                 texture=True, scalars='scalars')\n",
    "plotter.view_isometric()\n",
    "\n",
    "# shrink globe in the background\n",
    "\n",
    "\n",
    "def shrink():\n",
    "    for i in range(50):\n",
    "        globe.points *= 0.95\n",
    "        # Update scalars\n",
    "        globe.point_arrays['scalars'] = np.random.rand(globe.n_points)\n",
    "        time.sleep(0.5)\n",
    "\n",
    "\n",
    "thread = Thread(target=shrink)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyvista as pv\n",
    "sphere1 = pv.Sphere(radius=0.1, center=(0, 0, 0))\n",
    "sphere2 = pv.Sphere(radius=0.1, center=(0, 0, 1))\n",
    "data = [sphere1,sphere2]\n",
    "blocks = pv.MultiBlock(data)\n",
    "blocks.plot(jupyter_backend='static',show_axes=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyvista as pv\n",
    "import numpy as np\n",
    "import time\n",
    "start_time=time.time()\n",
    "data = [pv.Sphere(radius=0.01, center=(np.random.random(), np.random.random(), np.random.random())) for i in range(1000)]\n",
    "blocks = pv.MultiBlock(data)\n",
    "blocks.plot(jupyter_backend='static',show_axes=1)\n",
    "end_time=time.time()\n",
    "print ('The time cost of ploting {} spheres is: {}s'.format(len(data),end_time-start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyvista as pv\n",
    "sphere = pv.Sphere()\n",
    "plotter = pv.Plotter(notebook=True)\n",
    "plotter.add_mesh(sphere)\n",
    "plotter.show(jupyter_backend='ipygany')\n"
   ]
  }
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